TECHNOLOGICAL CAPABILITY AND INTER- ORGANIZATIONAL COLLABORATION IN AN EMERGING ECONOMY
A thesis submitted to The University of Manchester for the degree of Doctor of Philosophy in the Faculty of Humanities
Rajenthyran Ayavoo
2019
Manchester Institute of Innovation Research (MIOIR) Innovation, Management and Policy (IMP) Alliance Manchester Business School University of Manchester
1 TABLE OF CONTENTS
LIST OF TABLE 5
LIST OF FIGURES 6
ABSTRACT 7
DECLARATION 8
COPYRIGHT STATEMENT 9
ACKNOWLEDGEMENT 10
ABBREVIATIONS 11
CHAPTER 1: INTRODUCTION 13 1.1 OVERVIEW 13 1.2 RESEARCH PROBLEM 15 1.3 RESEARCH QUESTIONS 17 1.4 OBJECTIVE AND AIMS OF THE RESEARCH 18 1.5 RESEARCH CONTEXT 20 1.6 STRUCTURE OF THE THESIS 21
CHAPTER 2: LITERATURE REVIEW 25 2.1 INTRODUCTION 25 2.2 NATURE OF TECHNOLOGICAL CAPABILITY IN EMERGING ECONOMIES FROM THE PERSPECTIVE OF EVOLUTIONARY THEORY 25 2.3 TECHNOLOGICAL CAPABILITY DEVELOPMENT IN EMERGING ECONOMIES 32 2.3.1 TECHNOLOGICAL LEARNING 38 2.4 WHY DOES INTER-ORGANIZATIONAL COLLABORATION MATTER IN EMERGING ECONOMIES? 41 2.4.1 PREVIOUS EMPIRICAL RESEARCH 43 2.5 RESEARCH GAP 49
CHAPTER 3: INTER-ORGANIZATIONAL COLLABORATION AND TECHNOLOGICAL CAPABILITY BUILDING 54 3.1 INTRODUCTION 54 3.2 WHY DO FIRMS IN EMERGING ECONOMIES ENGAGE IN IOC? 54 3.2.1 IOC BREADTH AND DEPTH 58 3.3 CONCEPTUAL FRAMEWORK 64 3.4 RESEARCH HYPOTHESES 72 3.4.1 HYPOTHESES RELATING TO IOC: BREADTH & DEPTH AND TC BUILDING (H1 – H3). 72 3.4.2 HYPOTHESES RELATING TO DIFFERENT ORGANIZATIONAL PARTNERS: IOC-DEPTH AND TC BUILDING (H4 - H10). 82
CHAPTER 4: RESEARCH METHODOLOGY 94 4.1 INTRODUCTION 94 4.2 RESEARCH STRATEGY 95 4.2.1 RESEARCH PHILOSOPHY 95 4.3 RESEARCH METHODOLOGY 97 4.3.1 MIXED-METHOD STRATEGY 99 4.3.2 TRIANGULATION 100
2 4.4 QUANTITATIVE APPROACH 102 4.4.1 DATA COLLECTION 102 4.4.2 VARIABLES AND MEASURES 109 4.5 QUALITATIVE APPROACH 120 4.5.1 BACKGROUND 120 4.5.2 DATA COLLECTION AND INTERVIEW STRATEGY 121 4.5.3. QUALITATIVE PHASE DATA ANALYSIS 125 4.6. CHAPTER SUMMARY 129
CHAPTER 5: AN OVERVIEW OF MALAYSIA’S INNOVATION POLICY AND NATIONAL INNOVATION SYSTEM (NIS) 131 5.1 INTRODUCTION 131 5.2 INDUSTRY LANDSCAPE OF THE MANUFACTURING FIRMS IN MALAYSIA. 132 5.3 MALAYSIAN’S INNOVATION POLICIES 135 5.3.1 THE EVOLUTION OF INDUSTRIAL POLICIES IN MALAYSIA 138 5.3.2 THE EMERGENCE OF A SCIENCE, TECHNOLOGY & INNOVATION (STI) POLICY 144 5.3.3 INDUSTRY 4WRD: NATIONAL POLICY ON INDUSTRY 4.0. (YEAR 2018-2025) 147 5.4 MALAYSIAN INNOVATION POLICIES MIX: KEY ISSUES AND PROBLEMS 149 5.5 CHAPTER SUMMARY 156
CHAPTER 6: QUANTITATIVE FINDINGS 159 6.1 INTRODUCTION 159 6.2 DESCRIPTIVE STATISTICS OF SAMPLE 160 6.2.1 TC INPUT 160 6.2.2 TC OUTPUT 165 6.2.3 IOC WITH EXTERNAL ORGANIZATIONAL CHANNELS 170 6.3 HYPOTHESIS TESTING 175 6.3.1 LOGISTIC REGRESSION 175 6.3.2 MODEL SPECIFICATION 178 6.4 RESULTS OF HYPOTHESIS TESTING 180 6.4.1 IOC-BREADTH AND IOC-DEPTH, AND TC BUILDING (HYPOTHESES 1-3). 180 6.4.2 IMPACT OF DIFFERENT PARTNERS: IOC-DDEPTH AND TC (HYPOTHESES 4-10). 185 6.5 ROBUSTNESS TESTS AND ALTERNATIVE EXPLANATIONS 192 6.5.1 ALTERNATIVE TO DEPENDENT VARIABLES 192 6.5.2 ALTERNATIVE TO INDEPENDENT VARIABLES 196 6.5.3 PROBIT MODEL 199 6.6 CHAPTER SUMMARY 201
CHAPTER 7: QUALITATIVE FINDINGS 203 7.1 INTRODUCTION 203 7.2 INTERVIEW RESULTS 203 7.2.1 ISSUE: IOC-BREADTH (H1) 203 7.2.2 ISSUE: IOC DEPTH (H2) 205 7.2.3 ISSUE: IOC-DEPTH OVER IOC-BREADTH (H3) 206 7.2.4 ISSUE: COLLABORATION WITH CUSTOMERS & SUPPLIERS (H4 AND H5) 207 7.2.5 ISSUE: COLLABORATION WITH COMPETITORS (H 6) 209 7.2.6 ISSUE: CONSULTANTS AND PRIVATE R&D INSTITUTES (H7 AND H8) 212 7.2.7 ISSUE: UNIVERSITIES AND GOVERNMENT RESEARCH INSTITUTIONS (H 9 AND H10) 214 7.2.8 OTHER ISSUES 216 7.3 CASE STUDIES ANALYSIS: TECHNOLOGICAL CAPABILITY BUILDING 218 7.3.1 CASE STUDY A: EELHT-2 219 7.3.2 CASE STUDY B: ESMEHT-5 226
3 7.3.3 CASE STUDY 3: FFSMELMT-15 232 7.3.4 CASE STUDIES SUMMARY 238 7.4 CHAPTER SUMMARY 241
CHAPTER 8: DISCUSSION 244 8.1 INTRODUCTION 244 8.2 DISCUSSION OF FINDINGS 244 8.2.1 OVERVIEW OF THE MAIN FINDINGS 244 8.2.2 RELATIONSHIP BETWEEN IOC AND TC 245 8.2.3 IMPACT OF DIFFERENT ORGANIZATIONAL PARTNERS: IOC-DEPTH AND TC 253 8.2.3 LARGE FIRMS AND SMES TC DEVELOPMENT 264 8.3 CHAPTER SUMMARY 265
CHAPTER 9: CONTRIBUTIONS, LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH 268 9.1 INTRODUCTION 268 9.2 CONTRIBUTIONS TO THE FIELD OF STUDY 269 9.2.1 THEORETICAL CONTRIBUTIONS 269 9.2.2 EMPIRICAL CONTRIBUTIONS 271 9.2.3 PRACTICAL CONTRIBUTIONS 272 9.2.4 MANAGEMENT AND POLICY CONTRIBUTIONS 274 9.3 LIMITATIONS OF THE RESEARCH 275 9.4 DIRECTIONS FOR FUTURE RESEARCH 278 9.5 CONCLUSION 279
REFERENCES 280
APPENDICES 296
Word count: 84,964
4 LIST OF TABLE
Table 2.1: Matrix of Technological Capabilities. ______34 Table 4.1: Innovative and non-innovative manufacturing firms. ______108 Table 4.2: Collaboration with Organizational Partners (importance of partners) for innovation activities of Malaysian firms (n=445). ______113 Table 4.3: Define the Independent variables based on Innovation Survey (e.g. UK CIS) ______114 Table 4.4: The main industry type of the analysis. ______116 Table 4.5: Innovative firms based on firm size. ______117 Table 4.6: Innovative firms based on firm year of establishment. ______118 Table 4.7: The main type of ownership of the analysis. ______119 Table 4.8: List of Interviews of Manufacturing Firms. ______123 Table 4.9: Profile of Firms Interviewed for Case Studies. ______129 Table 5.1: Malaysia’s Science, Technology and Innovation (STI) development stages from 1960 to 2015. ___ 137 Table 5.2: The STI initiatives in Malaysia between 1985 and 2010. ______147 Table 6.1: Technological capability input (R&D activities) of Malaysian manufacturing firms during the three year period from 2009 to 2011 (n=445). ______161 Table 6.2: Technological capability outputs of Malaysian manufacturing firms during the three year period from 2009 to 2011 (n=445). ______166 Table 6.3: Inter-organizational collaboration for innovation with external organizational channels (importance of co-operating partners) of Malaysian manufacturing firms during the three year period from 2009 to 2011 (n=445). ______171 Table 6.4: Hypotheses of the Research. ______178 Table 6.5: Logistic regression analysis (weighted results) of TC-output and IOC (breadth and depth). ______184 Table 6.6: Logistic regression analysis (weighted results) of TC-input and IOC (breadth and depth). ______184 Table 6.7: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. 191 Table 6.8: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. _ 191 Table 6.9: Logistic regression analysis (weighted results) of TC-output and IOC (breadth and depth). ______193 Table 6.10: Logistic regression analysis (weighted results) of TC-input and IOC (breadth and depth). ______193 Table 6.11: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. ______194 Table 6.12: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. 194 Table 6.13: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. ______195 Table 6.14: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. 195 Table 6.15: Logistic regression analysis (weighted results) of TC-output and IOC (breadth and depth). _____ 196 Table 6.16: Logistic regression analysis (weighted results) of TC-input and IOC (breadth and depth). ______196 Table 6.17: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. ______197 Table 6.18: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. 197 Table 6.19: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. ______198 Table 6.20: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. 198 Table 6.21: Logistic regression analysis (weighted results) of TC-output and IOC (breadth and depth). _____ 199 Table 6.22: Logistic regression analysis (weighted results) of TC-input and IOC (breadth and depth). ______199 Table 6.23: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. ______200 Table 6.24: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. 200 Table 6.25: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. ______201 Table 6.26: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. 201 Table 7.1: Summary of Research Results Hypotheses 1-10 ______217 Table 7.2: Profile of Firms Interviewed for Case Studies. ______218
5 LIST OF FIGURES
Figure 3.1: Framework linking IOC: (Breadth & Depth) and TC______70 Figure 3.2: Conceptual Framework______71 Figure 4.0: Methodological Framework (Mixed-Method Approach)______98 Figure 4.1: Innovative and non-innovative manufacturing firms______108 Figure 4.2: The main industry type of analysis______116 Figure 4.3: Total innovative firms based on firm size______117 Figure 4.4: Total innovative firms based on year of establishment______118 Figure 4.5: Total innovative firms based on the type of ownership______119 Figure 6.1 Compares TC input activities of Low - Medium Technology (n=226) and High Technology firms (n=219) between year 2009 to 2011______162 Figure 6.2 Compares TC input activities of Medium-sized enterprises (n=258) and Large firms (n=187) between year 2009 to 2011______163 Figure 6.3 Compares TC input activities of the Established firms (year of establishment before 2000) and New firms (year of establishment after 2000) between year 2009 to 2011______164 Figure 6.4 Compares TC input activities of Foreign firm (n=380; headquarters located outside Malaysia) and Local firm (n=65; headquarters located in Malaysia) between year 2009 to 2011______165 Figure 6.5 Compares TC output activities of Low/Medium Technology (n=226) and High Technology firms (n=219) between year 2009 to 2011______167 Figure 6.6 Compares TC output activities of Medium-sized enterprises (n=258) and Large firms (n=187) between year 2009 to 2011______168 Figure 6.7 Compares TC output activities of Established firms (year of establishment before 2000) and New firms (year of establishment after 2000) between year 2009 to 2011______169 Figure 6.8 Compares TC output activities of Foreign firm (n=380; headquarters located outside Malaysia) and Local firm (n=65; headquarters located in Malaysia) between year 2009 to 2011___170 Figure 6.9 Compares the inter-organizational collaboration activities of LMTs (n=226) and high technology firms (n=219)______172 Figure 6.10 Compares the inter-organizational collaboration activities of SMEs (n=258) and large firms (n=187)______173 Figure 6.11 Compares the inter-organizational collaboration activities of Established firm (year of establishment before 2000, n=197) and New firm (year of establishment after 2000, n=248)______174 Figure 6.12 Compares the inter-organizational collaboration activities of foreign firm (headquarters located outside Malaysia, n=65) and national firms (headquarters located in Malaysia, n=380)______175
6 The University of Manchester Rajenthyran Ayavoo Doctor of Philosophy
Technological Capability and Inter-Organizational Collaboration in an Emerging Economy
2019
ABSTRACT Technological capability (TC) is an important source of competitive advantage for firms in emerging economies. The economic performance of Newly Industrializing Countries (NICs) - four Asian Tigers, Hong Kong, Singapore, South Korea and Taiwan have developed relatively strong TC, which is the major factor for rapid export growth and economic development. Technology and TC have become the centre of competition in the world market. The development of TC has been studied in a large body of literature. However, TC building in emerging economies was not clearly understood, in particularly how firm develop TC remain under explore area of research in the context of emerging economies. Therefore, the main aim of this thesis is to examine to what extent formal inter- organizational collaboration (IOC) affects TC building in emerging economies. It focuses on the relationship with TC building of two collaboration strategies: IOC-breadth and IOC-depth, and also investigates the influence of IOC-depth on TC development with suppliers, customers, competitors, consultants, private R&D, universities and government research institutions. The research employed a mixed-method approach. For the quantitative method, firm-level data from 445 Malaysian manufacturing firms were collected from the sixth series of the Malaysian National Survey of Innovation; and the qualitative approach undertook 30 in-depth interviews from 15 manufacturing firms’ senior managers and two policy-makers. The results have revealed that IOC for innovation plays an important role in firms’ TC building in emerging economies. Both IOC-breadth and IOC- depth are important collaboration strategies, especially IOC-depth, with customers, suppliers, and consultants as the most important partners for TC development. IOC-depth with universities and government research institutions is relatively weak, and with competitors and private research institutes is inadvisable because of the risk associated with unplanned knowledge spillovers. SMEs tends to trade-off wider collaboration (IOC-breadth) in favour of deeper collaboration (IOC-depth) for their TC development. This research contributes to the theory of evolutionary economics by linking two streams of literature, TC and IOC, from the perspective of an emerging economy. This theory, in both sets of literature, highlights the significance of IOC with different partners for firms’ TC development. This research also contributes to the literature through understanding the nature of TC development in emerging economies and mapped relevant policy implications in Malaysia and even in other developing countries.
Keywords: Technological capability, inter-organizational collaboration, IOC breadth, IOC depth, emerging economies, evolutionary economics theory, innovation survey, Malaysian National Survey of Innovation.
7 DECLARATION
No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institute of learning.
8 COPYRIGHT STATEMENT i. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. iii. The ownership of certain Copyright, patents, designs, trademarks and other intellectual property (the “Intellectual Property”) and any reproductions of copyright works in the thesis, for example graphs and tables (“Reproductions”), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialization of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=2442 0), in any relevant Thesis restriction declarations deposited in the University Library, The University Library’s regulations (see http://www.library.manchester.ac.uk/about/regulations/) and in The University’s policy on Presentation of Theses
9 ACKNOWLEDGEMENT
This PhD thesis is dedicated to my late father, Ayavoo Palaniyppan.
I would like to show gratitude to many people who have assisted me directly throughout the production of this thesis. First and foremost, I wish to express my most profound appreciation to Dr. Ronald Ramlogan (Ronnie Ramlogan) who has guided me more than as a supervisor. Whenever I had a concern, his door was always open for discussion. He spent valuable time and provided significant levels of advice and feedback on all my writing. His intellectual guidance has encouraged me throughout all of my research and writing of this thesis. I could not have imagined having a better advisor and mentor for my PhD study.
I also would like to thank my first and second supervisors Prof. Marcela Miozzo and Dr. Elvira Uyarra who always provided feedback and continuously supported my every effort in this research. Their patience, motivation, enthusiasm, and immense knowledge helped me throughout my PhD study. My sincere thanks also go to Dr. Reza Salehnejad, for helping me with the data analysis.
My special and deepest appreciation and thanks are due to the many members of my family, to whom I owe so much. First, I give sincere gratitude to my mother Letchimy who wanted me to be an academic and always prayed for my success. In addition, I am indebted to my brothers and sister (Radhakrishnan, Cachithaananthan, Tinagaran and Jaya Uasanth Kumary) for their continuous support during this crucial and challenging time of my life, and throughout this study. I would not be where I am today without their understanding and support. I must acknowledge my friend Egena Ode, for his support, suggestions, and encouragement.
Finally, I wish to dedicate this thesis to my beloved wife, Logeswary, with my sincere gratitude. Her endless love and patience encouraged me to go to the end of this challenging journey. Without her, I would not have been able to start or complete this journey. I am especially thankful to my lovely little boy, Jaashwienraaj Rajenthyran, who enabled me to reach beyond my capacity.
Finally, I thank God for all things bright and beautiful and good health. “Ella Pugallum Iraivanukke”
10 ABBREVIATIONS
AC Absorptive Capacity AIC Akaike Information Criterion APIDT Action Plan for Industrial Technology Development BIC Bayesian Information Criterion CA Companies Act CEO Chief Executive Officer CIS Community Innovation Survey DOSM Department of Statistics Malaysia EDI Electronic Data Interchange E&E Electrical and Electronic EPU Economic Planning Unit EU European Union FDI Foreign Direct Investment GDP Gross Domestic Product GPS Global Positioning System HICOM Heavy Industries Corporation of Malaysia High-tech High Technology ICT Information and Communications Technology IMP Industrial Master Plan IMPs Industrial Master Plans IP Intellectual Property IPRs Intellectual Property Rights IOC Inter-Organizational Collaboration KIS Korean Innovation Survey KIBS Knowledge Intensive Business Services KBV Knowledge Based View LMT Low and Medium Technology LCD Liquid Crystal Display OECD Organization for Economic Co-operation and Development OLS Ordinary least squares PIO Pioneer Industrial Ordinance MBC Malaysia Business Council METR Ministry of Energy, Technology and Research MIDA Malaysian Industrial Development Authority MIDF Malaysian Industrial Development Finance MIGHT Malaysia Industry-Government Group for High Technology MITI Ministry of International Trade and Industry MNSI Malaysian National Survey of Innovation MOSTE Ministry of Science, Technology and Environment MNC Multinational Corporation MSC Multimedia Super Corridor NCSRD National Council for Scientific Research and Development NEM New Economic Model NICs Newly Industrializing Countries NIS National Innovation Systems NEPAD New Partnership for African Development NSTP National Science and Technology Policy RBV Resource Based View R&D Research and Development SIRIM Standards and Industrial Research Institute of Malaysia
11 SME Small and Medium-sized Enterprise SMEs Small and Medium-sized Enterprises SOP Standard Operating Procedures STI Science, Technology and Innovation TC Technological Capability TCs Technological Capabilities UK United Kingdom USA United States of America UTM Universiti Technology Malaysia UM Universiti Malaya α Cronbach’s coefficient alpha χ2 Chi-Square χ2/df Normed Chi-Square
12 CHAPTER 1: INTRODUCTION
1.1 Overview
This dissertation examines to what extent formal inter-organizational collaboration (IOC) affects technological capability (TC) building in emerging economies. The TC development in emerging economies is recognized as one of the most critical resources providing sustainable competitive advantage (Lall, 1992, 1994, 2000; Bell and Pavitt, 1993; Kim, 1997,
2000; Molina-Domene, 2012; Figueiredo, 2017; Hansen and Lema, 2019). Technical advancement is a key driving force and an important source of economic and social development (Nelson, 1987; Wignaraja, 1998; Pietrobelli, 1998). Furthermore, technology and technological capability have become a fundamental element of competition on the global stage. In other words, it is a necessity for firms’ survival in the global market and in competing with international and multi-national companies (MNCs). The growth, assimilation and further development of the latest technology are determined by the patterns of co-operation, competition and trade of a firm (Lall, 1992, 2000). The capacity for accessing and inventing new technology thus affects their ability of firms to develop an indigenous TC and survive on the international stage (Lall, 1992, 1995; Bell and Pavitt, 1993,
1995; Bell and Figueiredo, 2012; Hansen and Ockwell 2014). Hence, TC has become the centre of attention not only among researchers but also among stakeholders, corporate figures and policymakers (Hobday, 1995; Kim, 1997; Wong, 1999; Chuang, 2016; Karabag, 2019).
Firms in emerging economies operate extensively in a highly competitive environment.
Emerging economies put significant effort to achieve the status of “industrialized world” or
“high-income country”. Countries like Malaysia, Argentina, China, and Indonesia are in the early stages of industrial development (World Bank, 2014; World Economic Outlook, 2014).
Industrial development is acknowledged as a process of building up TC and transfiguring
13 them into product and process inventions corresponding with continuous technical and technological upgrading (Kim et al., 2000; Lall, 2000; Miozzo and Walsh, 2006; Shan and
Jolly, 2013). In other words, the process of industrial development is about the process of developing TC, which plays an important role in building up the competitive advantage of an organization, sector, and also the country. TC has also been established as a pervasive factor of production in the future (Lall, 1992; Wignaraja, 1998). For example, the economic performance of South-East Asian countries, especially those known as Newly Industrializing
Countries (NICs) or the four Asian Tigers – Hong Kong, Singapore, South Korea and Taiwan
– have developed relatively strong technological capabilities to accommodate domestic and international standards, the key determinant of their rapid economic growth and technological advancement (Hobday, 1994, 1995; Pietrobelli, 1998; Wignaraja, 2001; Tsai, 2004). Thus, the development of TC is vital for firms, especially manufacturing firms in emerging economies such as Malaysia, which are in a catch-up phase of industrialization.
This chapter outlines the overall structure of the thesis, starting with an overview of the research background. This followed by a discussion of the research problem, which identifies the gaps in the literature. The research questions and objectives of the study then presented.
The next section highlights the research context, with an overview of Malaysia and its manufacturing sector. The last section outlines the overall structure of the thesis.
14 1.2 Research Problem
TC has been recognized as an important source of competitive advantage and industrial development for emerging economies. The level of export growth and economic development of a nation largely depend on the ability to develop TC. However, the question of how firms build TC has attracted significant attention from innovation studies since the
1980s. Although the development of TC has been studied in a large body of literature (e.g.
Lall, 1992, 2000; Bell and Pavitt, 1993, 1995; Kim, 1997, 2000; Wong, 1999; Dosi et al.
2008; Marcelle, 2012), TC building in emerging economies is still not clearly understood
(Katz, 1984, Lall, 1992; Wignaraja, 1998; Molina-Domene, 2012; Hansen and Ockwell,
2014; Chuang, 2016; Figueiredo, 2017), and more recently by Hansen and Lema, (2019).
This is largely because of the nature of TC building in emerging economies is more complex and challenging process, but also because of the fast-changing frontiers of technology.
Technological knowledge is not shared equally among firms, nor is it readily imitated by or transferred across enterprises or industries like a physical product is because it has a sizeable tacit element (e.g. Nelson and Winter, 1982; Dosi, 1988; Wignaraja, 1998; Miozzo and
Walsh, 2006; Marcelle, 2012). There is little doubt that as a description of reality, in both developing and emerging countries, evolutionary theory is far more realistic than neoclassical theory (production function approach) in understanding the nature of TC building in emerging economies.
A large body of the literature agrees that firms in emerging economies are “rarely able” or
“not able” to develop TC on their own. For example, Lall (1993b, 1995) and Wignaraja
(1998) stated that manufacturing firms do not develop TC in isolation; “technological learning in a firm does not take place in isolation, the process is rife with externalities and interlinkages” (Lall, 2000: 19). Evolutionary economists have pointed out that IOC with
15 external channels allows the organization to access different sources of information and to develop new combinations of technical knowledge and capabilities (Nelson and Winter,
1982). Such variations provide greater opportunities for firms to select among different technological co-operation (Metcalfe, 1994; Metcalfe and Ramlogan 2008). IOC with external institutions is important for increasing the knowledge base for TC building. Lall
(1993b) stated that the promotion of linkages and the development of institutions to undertake activities beyond the scope of individual firms had become a vital part of TC development. TC is created as a result of the intense interaction between firms and other organizations (Bell and Pavitt, 1993, 1995; Wignaraja, 1998; Zhou and Wu, 2010). Although several studies have recognized that external technological learning and IOC are important for TC building in emerging economies, there is less in-depth research, especially quantitative studies, focusing on whether IOC for innovation leads to TC building. Bell and
Figueiredo (2012: 36) claim that the majority of studies on technological learning and capability building in emerging economies are based on a single method - qualitative designs
(e.g. see Figueiredo, 2017).
A critical review of the literature on technological learning and capability building clearly indicates that IOC is an important strategy for firms in emerging economies to develop TC, and the literature on IOC sheds new light on the TC building approach in emerging economies. Previous empirical research on IOC literature mainly concentrates on two major issues: the first is about determinants for IOC, or motives for collaboration, and the second focus on the relationship between IOC and different innovation activities or performance.
However, the literature on TC building in emerging economies pays limited attention to the evolutionary theory of firm-level external technological learning, especially learning by interacting or collaboration with external partners (IOC).
16
In order to have a broader understanding of this issue from evolutionary economic theory, this research examines the influence of IOC on TC building in emerging economies, specifically in the context of Malaysian manufacturing firms. Few studies have examined this relationship in a systematic way (quantitative research), using a large firm-level dataset.
Therefore, the main aim of this thesis is to investigate the influence of IOC on TC building in emerging economies, using a mixed-method approach. For the quantitative method, firm- level data of Malaysian manufacturing firms were collected from the sixth series of the
Malaysian National Survey of Innovation (MNSI-6); the qualitative approach was used in in- depth interviews with selective manufacturing firms’ top managers and policy-makers.
Further, three case studies were developed from three individual firms to show how firms are implementing collaborations with external organizations to build their TC.
1.3 Research Questions
The primary focus of this study is to examine to what extent formal IOC with external partners affects TC building in emerging economies. In other words, do firms’ in emerging economies able to develop their TC through collaboration with external organizations or partners? In particular, it focuses on two collaboration strategies: inter-organizational collaboration breadth (IOC-breadth) and inter-organizational collaboration depth (IOC-depth) relationship with TC building. Do the intensity of collaboration and collaborating partners are matter for TC development in emerging economies? Therefore, this research investigates the influence of IOC-depth on TC development with suppliers, customers, competitors, consultants, private R&D, universities, and government research institutions. It explores evidence from Malaysian manufacturing firms to understand the context of this phenomenon
17 in emerging economies. Therefore, the following research questions have been constructed to guide this study:
To what extent does formal IOC affect TC building in emerging economies? In particular, what is the relationship between IOC-breadth and IOC-depth with different partners and their impact on TC building in emerging economies? Based on these questions, the theoretical framework and ten hypotheses have been formulated, discussed more details in Chapter 3.
1.4 Objective and Aims of the Research
TC development in emerging economies recognized as one of the most critical resources for industrial development. It provides a sustainable competitive advantage to survival in the global market and to compete with international companies. Similarly, the economic performance of Newly Industrializing Countries (NICs) - four Asian Tigers, Hong Kong,
Singapore, South Korea, and Taiwan have developed relatively stable technological capabilities to accommodate domestic and international standards, a key determinant of their for rapid export growth and economic development. Malaysia is also known as the new Asian tiger (second-tier newly industrializing countries) along with Indonesia, Thailand, and the
Philippines (Felker and Jomo, 1999; Wong, 1999; Lall, 2000). However, Malaysia, like other emerging economies has been stuck in the middle-income trap or catch-up phase of industrialization. Most emerging and developing countries faced significant challenges to develop their TC. Technological development in emerging economies is far more complex and challenging process. Therefore, the main aim of this research to find out do firms’ in emerging economies able to develop their TC through collaboration with external
18 organizations? Do the intensity of collaboration and collaborating partners are matter for TC development in emerging economies?
This thesis seeks to provide a deeper understanding of the way in which external collaboration contributes to the development of TC in emerging economies. In particular, do
Malaysian firms build their TC through IOC? Do IOC-breadth and IOC-depth affect the development of TC and which has the greater effect? Further, which external IOC-depth partners have the stronger influence on TC development? What particular challenges do firms in emerging economies, especially Malaysia, face regarding IOC and the development of
TC? Are any significant differences between large firms and SMEs TC development in
Malaysia?
In order to meet the above research objective and aim (answer the research questions), this thesis employed a mixed-method approach. First, the quantitative analysis is based on firm- level data from MNIS-6 used to test ten hypotheses. Second, qualitative results obtained from the 30 semi-structured interviews from 15 manufacturing firms and two policy-makers are used to support the quantitative results and expected to provide in-depth insight and further explanation of the relationship between TC building and IOC. Lastly, three case studies were developed from three individual firms to show how firms are implementing collaborations when dealing with the issue of building technological capability via external partnerships. To ensure the validity of this research; the quantitative, interviews and case studies results, along with secondary data - consisted of company annual reports, industry websites, industry journals, newspapers, business magazines, and industry association publications were used to triangulate the overall research findings to meet the objective and aim of this thesis.
19 1.5 Research Context
Malaysia is an Asian upper-middle income country with a GDP of $314.5 billion (USD) in
2017 (World Bank Report, 2017), with a total area of 329,847 square kilometers and a population over 32 million (World Bank and United Nations Report, 2017). Before the
1970s, Malaysia had an agriculture and mining-based economy. The history of Malaysia’s economic development since independence in 1957 is impressive (OECD, 2016). From an agrarian-based economy entirely dependent on primary commodities, the country has successfully become a multi-sector economy with manufacturing and services, including heavy industrialization encouraging economic growth. Malaysia’s GDP from the manufacturing sector increased from 14% to 30% from 1971 to 1993, while the agriculture- based input declined from 43% to 24%. Malaysia was one of 13 countries identified by the
Commission on Growth and Development in its 2008 Growth Report to have recorded an average growth of more than 7% a year over 25 years. More recently, the Malaysian government has put more emphasis on developing a knowledge- and innovation-driven economy to confirm the status of an industrialized country and to catch up with other developed countries.
Why the Malaysian manufacturing sector?
The manufacturing sector did exist during the early colonial period, but its contribution to the national income was only around 8%. In the 1930s the manufacturing sector became an important contributor to national GDP, processing half of the world’s tin production. In the
1940s, the export of tin and rubber grew significantly. By 1960, the production of basic metals, textiles, and electrical machinery had grown rapidly, with an annual turnover representing over 30% of GDP. Since then, the manufacturing sector has become the major contributor. The Malaysian GDP increased by 7.2% from 2009 to 2010, with the
20 manufacturing sector contributing 27% (Malaysia Department of Statistics 2010). By 2017, this figure was around 23.2%, but it represented 80% of total exports, making Malaysia the
17th largest exporting nation in the world (Malaysia Department of Statistics, 2017; World
Bank Report, 2017). The manufacturing industry not only plays a critical role in economic development and GDP but is also a key driving force for structural transformation into high value-added activities. The majority of new projects are in high value-added activities and high-growth industries, with manufacturing as the main engine for economic growth and industrial development.
1.6 Structure of the Thesis
This section outlines the overall structure of the thesis, which is organized into nine chapters.
A brief summary of each chapter is presented below:
Chapter 1: The first chapter provides an overview of the research background, research problems (gaps in the literature), and identifies the research questions and objectives of the study. It presents the research context, with an overview of Malaysia and its manufacturing sector.
Chapter 2: This chapter identifies and locates the research gap and research questions that emerged from an in-depth literature review on the evolutionary theory of the firm as related, first, to TC development and technological learning and then to IOC. The first section discusses the nature of TC in emerging economies from the perspective of evolutionary theory. This is followed by the literature review on TC development in emerging economies and why does inter-organizational collaboration matter. The last section identifies the research gap and research questions.
21
Chapter 3: This chapter presents the conceptual framework and development of the research hypotheses, which are the outcomes of the in-depth literature review in Chapter 2. It discusses why firms in emerging economies engage in IOC and describe the concepts of
IOC-breadth and IOC-depth. It discusses the relationship between IOC and technological capability, and the influence of IOC-breadth and IOC-depth on TC building. Finally, the ten research hypotheses are presented, concerning to the literature and theoretical justification.
Chapter 4: This chapter discusses the research methodology and methods adopted in examining the research questions. It begins with the research strategy, explaining the relevant research philosophy and consequent methodology; a mixed-methods approach was selected, with both quantitative and qualitative data collection and analysis. For the quantitative approach, 445 firm-level data adopted from the Malaysian National Survey of Innovation sixth series. Followed by, the research variables (dependent, independent and control) and measurement of the variables are explained in detail. For the qualitative approach, the data collected from 30 semi-structured interviews from 15 manufacturing firms and two policy- makers. Further, three case studies were developed based on three individual firms. Lastly, the findings from both quantitative and qualitative analysis were carefully triangulated along with secondary data (company annual reports, industry websites, association publications, newspapers, and business magazines) to achieve the overall research objectives.
Chapter 5: This chapter is concerned with Malaysia’s innovation policy and the key actors of
Malaysia’s national innovation system as related to TC building and IOC. It discusses the evolution of industrial policies, through Malaysia’s Industrial Master Plans (IMPs) and the
22 emergence of science, technology and innovation (STI) policy in the country. It identifies the key issues and problems from these policies, as related to TC building and IOC.
Chapter 6: This chapter discussed the quantitative findings of the ten hypotheses testing, and robustness and alternative models that relevant to this research. The following section summarizes the dependent and independent variables in both statistical and graphical form.
The main objective of the descriptive analysis is to provide preliminary information about the data collected and determine the characteristics and tendencies of the variables. This followed by a section explaining why logistic regression is the ideal choice for the analysis. The model specification identifies the regression equations relevant to each hypothesis, and the results of the ten hypotheses, robustness tests and alternative explanations are discussed.
Chapter 7: This chapter discussed the qualitative findings, including the case studies analysis, are used to answer the research of this thesis. This chapter divided into two major sections. The first section presented the interview results to validate the quantitative finding from Chapter 6 and also provide an in-deep understanding of research questions. The second section discussed three mini case studies, which developed based on three individual firms from fifteen firms. The main reason to deployed three case studies to show how Malaysian manufacturing firms are implementing collaborations when dealing with the issue of building technological capability via depth or breadth of partner relations. The three mini case studies provided a comprehensive picture of how individual firms building their TC through external collaboration.
23 Chapter 8:
This chapter outlines the overall findings of the research and connects both quantitative and qualitative results with the existing literature and the findings of previous studies. The first section discusses the findings of the relationship between IOC and TC building (hypotheses
1-3). The second section illustrates the impact of different organizational partners, IOC-depth and TC building (hypothesis 4-10). The third section discusses the differences between large firms and SMEs TC development. The last section summarizes the chapter.
Chapter 9: The last chapter presents the key contributions and theoretical insights to the field of study. It discusses the limitations that have emerged in the research and identifies some avenues for future research, which may extend knowledge in the context of emerging economies.
24 CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
This chapter aims to identify the research gap and research questions that emerge from the in- depth literature review. To do so, it discusses the literature on evolutionary theories of the firm, technological capability (TC) development, technological learning, and inter- organizational collaboration (IOC). There are four sections discussed in this chapter. Section
2.2 discusses the nature of TC in emerging economies from the perspective of evolutionary theory. Section 2.3 focused on the literature on TC development in emerging economies and followed by technological learning. Section 2.4 discusses why inter-organizational collaboration (IOC) matters in emerging economies and previous empirical research. Lastly,
Section 2.5 discusses the research gap.
2.2 Nature of Technological Capability in Emerging Economies from the Perspective of Evolutionary Theory The firm-level analysis of TC building in emerging economies has drawn inspiration from the evolutionary theory pioneered by Nelson and Winter (1982), further extended by Dosi,
Nelson, and Winter (2000) and related work by Lall (1992, 2000), and Metcalfe (1993).
Central to this theory is the idea that firms cannot be assumed to operate on the same levels of the production function. Metcalfe (1993: 214) claims that the organization, individuals’ involvement or historical circumstance means that two firms cannot innovate or build technological capability in an identical way, significant for the decentralized emergence of technical diversity, described in evolutionary economic theory.
Technological knowledge and resources are not shared equally among enterprises, nor are they readily imitated by or transferable across firms or industries like a physical product
(Nelson and Winter, 1982; Dosi, 1988; Wignaraja, 1998; Miozzo and Walsh, 2006; Bell and
25 Figueiredo, 2012; Kiamehr et al., 2013). Transfer and building TC certainly require learning because technical and technology aspects are largely tacit and their underlying principles are not always clearly understood (Lall, 1992; Wignaraja, 1998; Molina-Domene, 2012;
Hansen and Ockwell, 2014; Chuang, 2016; Figueiredo, 2017; Hansen and Lema, 2019). The tacit technological knowledge embodied in persons or organization is difficult to articulate, and even more difficult to write down or codify in a meaningful way. There are considerable amount of tacit elements in what is necessary to manage different technology. Nelson (1987:
84) stated that an enterprise might not be aware of all the things it can do, and certainly will not be able to show in a clear and detailed manner how it does what it does. Therefore, technologies are not merely a set of blueprints or guidelines, which simply can be followed and have the same innovation outputs (Wignaraja, 1998; Pietrobelli, 1998; Miozzo and
Walsh, 2006; Shan and Jolly, 2013). This implies that each firm has to exert considerable absorptive capacity to learn the tacit elements of technology and gain adequate mastery. The tacit elements of technological resources are prominent themselves in the rules of thumb, which can be obtained and enhanced only with experience over time (Pietrobelli, 1998; Bell and Figueiredo, 2012).
TC is not available to a firm easily or without cost (Nelson and Winter, 1982; Dosi, 1988;
Pietrobelli, 1998; Hansen and Lema, 2019), and technological transfer causes considerable challenges for adaptation and absorption (Bell and Pavitt, 1992; Bell and Figueiredo, 2012).
Firms do not directly choose the preferred option from the freely available technologies or select an approach that simply operates effectively at best (Pietrobelli, 1998; Wignaraja,
1998). Furthermore, the tacit aspects of technological knowledge and resources needed to improve products and processes innovation are also substantial, and the process of transfer takes more time and cost than transferring operating know-how (see Dosi et al., 2000; Lall,
26 2000; Marcelle, 2012). Differences in technology evolve over time across firms and across nations.
Furthermore, individual firms in emerging economies do not have complete knowledge of all the possible technological alternatives, their implications, and the skills and information they require (see Nelson and Winter, 1982; Lall, 1992; Pietrobelli, 1998; Hansen and Ockwell
2014). In other words, they are familiar with a point in the production function and have some knowledge of similar technologies, but very little knowledge of dissimilar technologies.
To the extent that technologies are tacit, firm production sets are fuzzy around the edges
(Nelson, 1987: 84). In the process of acquiring better technological knowledge and mastering the technology, firms make idiosyncratic changes and improvements in individual ways
(Nelson, 1987; Nelson and Winter, 1982; Schmitz 2006).
The search process that is necessary to make explicit the tacit elements of technology and absorb them is localized around a point of the production function (Atkinson and Stiglitz,
1969; Molina-Domene, 2012). Firms in emerging economies are characterized by deficient levels of absorptive capacity (Szogs, 2008; Lall, 1992), and the degree of localization is likely to be greater (see also Pietrobelli, 1998; Lall, 2000). New entrants in a setting that is typically less industrialized are likely to have limited knowledge of the possible alternative technologies. They also have limited skills to acquire and select them, and to extend their search to technologies distant from the one in use. In reality, the choice is from a restricted number of options, due to problems of factor indivisibilities, lack of information and limited factor substitutability. There is an element of uncertainty in the knowledge and the choice of technology. The critical point to note is that simply to gain mastery of new technology requires the recipient firm in a developing country to invest in developing new TC (and the
27 extent of technological proficiency varies by firm according to investments in TC building)
(Lall, 1992). Due to the very low level of absorptive capacity in emerging economies, intermediate organizations may play a crucial role as mediators between the local and foreign sources of knowledge and assist in the process of transferring knowledge and assimilating and adapting it to broader domestic context (Szogs, 2008). Firm-level differences in technological effort and development may vary according to industry, size of firm or market, level of development, trade, and industrial strategies pursued.
There is little doubt that as a description of reality, in both developed and emerging countries, evolutionary theory is far more realistic than neoclassical theory (production function approach), especially in understanding the nature of TC building in emerging economies.
This is mainly because neoclassical theory assumes away TC building is a costly and lengthy process which is necessary for moving towards the production possibility frontier; technological knowledge is not shared equally among firms, nor is it readily imitated by or transferable across firms because of its large tacit element; and firms do not have a complete knowledge of all the possible technological alternatives, their implications, or the skills and information they required. The firm-level variation is also found in well-developed countries but tends to be far more dynamic and challenging among firms in emerging economies.
Therefore, evolutionary theory is more suitable for dealing with firm-level differences in TC building in the latter. There are four main reasons for poor TC building in emerging economies are highlighted in the literature.
First, firms in emerging economies are disconnected from leading industrial clusters or sources of technological innovation (international sources of technology, R&D, and world centers of science and innovation) and important marketing networks (Hobday, 1994, 1995;
28 Wong, 1999; Chuang, 2016). Most of the leading sources of technological innovation are located in advanced countries (like the United States and Japan) (see Hobday, 1995; Kim,
2000). This leaves firms behind in engineering, technical skills, R&D, and production processes, and also behind world technological frontiers as others forge ahead. For firms in emerging economies, the process of innovation and TC building often involves their first becoming familiar with various ways of acquiring knowledge even before they are able to apply this knowledge to production and then to innovation; and firms are also embedded in
“increasingly pervasive international networks of potential sources of technology” (Marcelle,
2012: 5). Schmitz (2006) claimed that these firms are dislocated from the international sources of technology, especially the producer - user loops that generate innovation, face difficulties in access to proprietary technology, and suffer from weak national or local support for innovation and TC building. In addition, emerging economies’ systems integrators are disconnected from the international chain of suppliers (see Lall, 1992; Bell and Pavitt, 1993; Kiamehr et al., 2013).
Secondly, enterprises in emerging economies encounter specific challenges arising from the speed, complexity, and extensive effects of technological change in international markets
(Marcelle 2004, 2012; Bell and Figueiredo, 2012; Hansen and Lema, 2019). The global technology frontier keeps moving forward continuously and indeed has moved very rapidly since the evolution of new core technologies such as microelectronics, information technologies and biotechnologies (Kumar and Siddharthan, 1997; Miozzo and Walsh, 2006).
Countries encounter varying degrees of difficulty in building up TC in different industrial sectors because of the nature and rate of change of the underlying technology; also, the capability of various industries may have different spillovers and linkages for learning in related sectors. The technology frontier in these technologies is moving at a rapid pace, not
29 allowing standardization. This creates never-ending critical problems for countries that are trying to catch up. There is increasing difficulty in undertaking technological capability accumulation activity as product and technology lifecycles mature, and new generations of technology are produced with component products and process systems that are becoming more complex (Bell and Pavitt, 1997). Emerging economies firms face difficulties since they often do not possess and/or use advanced science and technological knowledge spanning many fields.
Thirdly, in emerging economies, largely inefficiencies caused by poor resource allocation are widely observed (Pietrobelli, 1998; Wignaraja, 1998; Metcalfe and Ramlogan 2008; Shan and Jolly, 2013). As Bell and Figueiredo (2012) pointed out, firms typically have limited internal capabilities for exploiting available sources of technology to implement innovation in their own activities; and lack of related support industries and a poorly developed technological infrastructure in emerging economies (Hobday, 1994; Ulku, 2011; Hansen and
Ockwell 2014). Emerging economies firms, usually have only limited access to human capital, management skills and competences, their available absorptive capacity is somewhat inadequate. Due to the limited available absorptive capacity, companies in the system encounter limited opportunity to cooperate with the low absorptive capacity firms (Tidd et al.
2005; Szogs, 2008). Firms in these economies do not enjoy the advanced capabilities of technological leaders and have limited access to specialized producers of advanced knowledge, raising serious questions over whether catch-up is possible in complex capital goods (Kiamehr et al., 2013). Rosenberg (1963: 223) claims that countries that do not have a local capital goods sector tend to lack the “technological base of skills, knowledge, facilities, and organization upon which technical progress so largely depends”. However, even if resources are correctly allocated, they are often utilized inefficiently in a technical sense, and
30 this generates low levels of productivity (Pietrobelli, 1998; UNCTAD, 2003; Kumar and
Siddharthan, 1997). In some circumstances, firms are able to obtain foreign state-of-the-art technological knowledge; however, they fail to modify and adapt to existing TC due to poor technological infrastructure and technical skills in related fields. Further, firms suffer from a lack of related support from surrounding industrial and weak technological infrastructures caused for poor TC development (Lall, 1992; Hobday, 1994; Bell and Pavitt, 1997).
Weaknesses in local knowledge-creation institutions, including lack of up-to-date knowledge and information and specific technological resources lead to weak TC building (Bell and
Pavitt, 1997; Wignaraja, 1998). This may be due to universities, other educational or technical institutions being poorly equipped with technical knowledge and TC; and they may have weak co-operation relationships with international innovative suppliers and users, making it difficult for them to assess knowledge related to new technology and information from well-developed countries (see, for example, Hobday, 1994; Pietrobelli, 1998;
UNCTAD, 2003; Shan and Jolly, 2013).
Finally, another challenge is concerned with leading-edge markets and lack of demanding customers (Hobday, 1995; von Hippel, 1986; Pietrobelli, 1998). Firms in emerging economies are distanced from the main international markets to which they expect to supply their products or services. This is because all the potential markets are located in well- developed countries (Hobday, 1995), for example, in North America, Europe, and Japan
(Wong, 1999). Domestic markets tend to be small and underdeveloped, and customers less knowledgeable as they fail to acquire the latest technological information from foreign suppliers, markets, and users (see, for example, Hobday, 1995; Pietrobelli, 1998; Chen et al.
2010). For example, supplying small and underdeveloped markets, as opposed to the large markets of developed countries, limits both the resources and motivation to invest in
31 advanced capabilities, as returns on investment are highly uncertain (Freeman and Soete,
1997; Metcalfe and Ramlogan 2008; Kiamehr et al., 2013). Besides, local clients typically do not have advanced accumulated knowledge of systems, which makes advancing innovation difficult for these firms, compared with markets in a developed country (Hobday, 1995;
Wignaraja, 1998; UNCTAD, 2003; Ulku, 2011). The lack of extensive knowledge and market networks may limit the search space for innovative ideas and TC building, also hindering catching up with global technology. In summary, TC building in emerging economies is a far more complex and challenging process than might be expected.
2.3 Technological capability development in emerging economies
Given the numerous definitions related to TC in emerging economies, this research has chosen to refer technological capability (TC) as technical efforts to master new technologies, adapt them to local conditions, and then improve and exploit them. These efforts include skills, knowledge and experience, institutional structures, and interaction networks, which among other external organizations that linked to the firm. In an earlier study, Kim (1980) introduced the concept of “technological capability”, which he later referred to as “the ability to make effective use of technological knowledge in efforts to assimilate, use, adapt and change existing technologies” (Kim, 1997: 4), and more recently as the ability to make effective use of technological knowledge in production, engineering and innovation, that also enables a firm to create new technologies and to develop new products and processes in response to their changing economic environment (Kim, 2001: 297).
According to Lall (1992: 167-168), it is feasible to classify TCs according to the different functions that the technological activities perform as well as the degree of their complexity
(basic, intermediate and advanced levels), as shown in Table 2.1. As regards the functions,
32 Lall differentiates between investment, production, and linkage capabilities for firm-level
TCs.
Investment capabilities are defined as all the skills and competencies required before the investment is undertaken to identify the feasibility and profitability of a project, and to locate and purchase suitable (embodied and disembodied) technologies in order to design, build, and market new products and services (see Bell and Pavitt, 1993, 1995; Lall, 1995, 2000).
The skills and knowledge needed to assess the effectiveness of the implementation of new technologies include selection, evaluation, preparation, design, acquisition, and staff training.
Production capabilities are focused on the improvement of capital assets and the outcome of the overall process. According to Lall (1992), production capabilities comprise three components: (1) product engineering, (2) process engineering, and (3) industrial engineering.
They include the skills and knowledge necessary for the subsequent operation and improvement of the plant: adaptation and improvement, workflow scheduling, troubleshooting, inventory control, monitoring productivity, assimilation of the process and product technology, overall innovation coordination of various product development and innovation (see Bell and Pavitt, 1993, 1995; Lall, 2000).
Linkage capabilities refer to the “skills needed to transmit information, skills and technology to, and receive them from, component or raw material suppliers, subcontractors, consultants, service firms, and technology institutions” (Lall, 1992: 168). Such co-operative relationships help firms to gather not only the productive efficiency (allowing them to specialize in a particular area), but also to expand the development of technology through the
33 economy and deepening the industrial structure, which is critical for industrial development.
In every group of TC there are different levels of complexity. Hence, TC is used for
“routine”, “adaptive and replicative,” and “innovative and risky” activities.
Table 2.1: Matrix of Technological Capabilities.
Sources: Lall, 1992: 167; Bell and Pavitt, 1995.
TC building in emerging economies is considered as a slow incremental process, through which firms gradually build a minimum-level of TC through learning that enables them to carry out innovation activities. It is seen as “one of acquiring and improving on TCs rather than of innovating at frontiers of knowledge. This process essentially consists of learning to use and improve on technologies that already exist in advanced industrial economies” (Lall,
2000: 13). In short, TC development in this context should not be seen as the ability to undertake breakthroughs innovation (new to the world), but rather as a means to improve existing goods or develop products or services that are new to the firms or new to the market.
In order to develop TC, companies are required to make considerable investments in various types of input. From the firm-level TC development perspective, these inputs can be categorized into external and internal (see Lall, 1992; Lall et al., 1994). The external inputs
34 refer to individual skills, machinery, equipment, technical knowledge, licenses, and trainers, which can be obtained from the external environment. The internal inputs are the activities or development undertaken in-house through training, experimentation, and research. There are two interactive processes in acquiring inputs: (1) internal effort is affected by the external inputs, and (2) the capacity of a firm to search, access, and utilize external inputs is influenced by the nature and extent of internal efforts. TC development can be viewed as the result of investment in both external and internal inputs that market and enhance the firm’s
TC building. Five key features of TC building are highlighted in the literature.
First, the process of TC development is complex, risky, and unpredictable, even though the technologies are widely used globally (Lall, 1994, 2000; Pietrobelli, 1998; Wignaraja, 1998).
The complexity and risk factors are important elements of TC development since the range of outcomes related to such activities is not known with much accuracy. Obviously, all economic activities involve some risk and uncertainty. For example, the insurance markets enable the risk factors to be spread, but rarely can companies insure against failure in TC development. Firms can reduce the risk factors in several ways. They not only face the technical uncertainties of whether technological goals can be achieved, but also the investment and financial uncertainties associated with TC building. There is no foreseeable way of learning that companies can follow. Furthermore, the gestation period for TC development may be considerable; it can take a decade or more for new product, service or process to emerge; and external conditions such as the introduction of substitutes, government policies or capital availability can fundamentally change the risk-reward outlook
(e.g. see Bell and Figueiredo 2012; Lee 2016; Tumelero et al., 2018; Karabag, 2019). In developing countries, although the process of TC building can be learned, companies may not recognize what are lacking or how to approach to improve them (see Bell and Pavitt, 1993).
35 Second, the technical and technological learning process is essentially firm-specific (Bell and
Pavitt, 1993, 1997; Pietrobelli, 1998; Lall, 2000). Hence, firms that attempt to use new technologies are required to have some technical capability as an outcome of their manufacturing, since passive learning is part of TC building (Malerba, 1992; Bell and Pavitt
1993; Hansen and Ockwell 2014). For example, in simple industries, such as the production of textiles or garment manufacture for the local market, might efficient. However, passive learning is necessary for more complex technology of assemblies and productions to tackle the market demands (e.g. see Hansen and Ockwell 2014; Hansen and Lema, 2019). For more complex sectors, to reach best practice, as established in well-developed economies, takes a more extended period of time and is a more challenging process, sometimes taking years of research and development (e.g. see Lamin and Dunlap, 2011). The conscious and purposive learning process means that different firms can encounter various levels of technical and technological advancement, resulting in varying levels of capacity in applying the same technology (Scott-Kemmis and Chitravas, 2007; Bell and Figueiredo 2012).
Third, the development of TC is a cumulative process (Lall, 1992, 2000; Bell and Pavitt,
1993, 1995; Wignaraja, 1998; Figueiredo, 2017; Karabag, 2019). From the firm-specific view, which requires specialized knowledge, enterprises can hardly develop various ways of capability and competence simultaneously, nor can they readily make jumps into a completely new technological field. Instead, they tend to follow particular trajectories building upon past investments in TC. The firms start mastering simple TC, then proceed to more complex, interrelated technology. Once they are established on a specific trajectory, it is quite difficult for them to switch to a new technology, since it is costly and may not be optimal.
36 Fourth, innovation is part of the TC building process, but it is the last piece of the broad range of technological activities (Lall, 1992, 2000). As Pietrobelli (1998) highlighted, the primary concern of TC building in emerging economies is not acquiring the capabilities to innovate the product or services and process. The firms have to use acquired capability in existing technology: to develop better production facilities, to manufacture more effectively and efficiently, apply all the gained experience in production and investment to improve current technology and develop products or processes that are new to the firm.
Fifth, organizations are important actors in TC building and rarely develop it in isolation
(Lall, 1992, 2000; Bell and Pavitt, 1993; Pietrobelli, 1998; Wignaraja, 1998). TC is created as a result of the intense interaction between firms and external organizations (see Bell & Pavitt,
1993, 1995). These interactions involve customers in an input-output relationship, also known as vertical co-operation. The other interactions include competitors (horizontal co- operation) and institutional co-operation with private R&D, universities, and government research institutes. Interaction with these channels allows individual firms to access knowledge and facilities, and develop information, resources, skills, and standards that all enterprises require, but which no individual company will generate by itself. Hence, an essential part of the process of TC building includes setting up strong inter-organizational collaboration (IOC) and networks.
Thus, the major problem for firms is to master, adapt, and improve the acquired technologies.
Not all firms have the ability or capacity to adopt foreign technologies to the local conditions successfully. A large body of literature states that TC building is not a linear process (e.g.
Lall, 1992, 2000; Marcelle, 2004, 2012; Hansen and Lema, 2019). The hardware is available equally to all countries, but the disembodied aspects of technology cannot be bought or
37 transferred like physical products. “Unlike the sale of a good, where the transaction is complete when physical delivery has taken place, the successful transfer of technology can be a prolonged process, involving local learning to complete the transaction” (Lall, 2000:16). In summary, the process of TC building is considered to be a complex learning process implemented by firms through trial and error, in which they build up people skills, knowledge bases and achieve effectiveness in the process by implementing managerial routines and developing values that facilitate learning. Therefore, technological learning is required for effective TC building among firms in emerging economies.
2.3.1 Technological learning
Kim (2001) defined technological learning as the process of building and accumulating TC.
Firms learn over time, accumulate technological knowledge, and can progressively undertake new activities and acquire new capabilities. According to Lall (2000), technological learning involves purposive, conscious and incremental efforts to obtain knowledge, try new things, develop new expertise and operational routines, and build external collaboration.
Technological learning is a dynamic process in building TC.
According to Kim (1997: 4) in emerging economies “technological capability” can be employed interchangeably with ‘absorptive capacity’ (Cohen and Levinthal 1990): ability to recognize the value of new information, absorbing existing knowledge, assimilate it, and in turn generating new knowledge or apply it to commercial ends1. TC is developed through the process of technological learning and effective technological learning demands absorptive capacity, which has two critical elements: (1) existing knowledge base and (2) intensity of effort (Cohen and Levinthal, 1990; Kim 1997, 1998). An existing knowledge base is a critical
1 Absorptive capacity (AC) is a firm’s “ability to recognize the value of new, external information, assimilate it, and apply it to commercial ends” (Cohen and Levinthal,1990: 128).
38 platform in technological learning, as knowledge today influences learning processes and the nature of learning to develop knowledge tomorrow. The intensity of effort indicates the amount of energy spent by firms to solve problems. Indirectly exposing companies to relevant external knowledge is insufficient if they not put sufficient efforts to internalise it.
Hence, the higher the existing knowledge foundation and intensity of effort, the faster and depth is the spiral process of technological learning and TC building.
However, TC building in firms involves learning processes and activities from two major sources: (1) internal learning, that involves acquiring and developing new knowledge from various sources within the firm; and (2) external learning, which involves a number of ways whereby knowledge is acquired and intense effort to internalize it from sources outside the firm. This approach (internal and external learning) is widely accepted in the technological learning and capability building literature of developing and emerging countries (Lall, 1992,
1993, 2000; Bell & Pavitt, 1993, 1995; Kim, 1997, 1998, 2001; Figueiredo, 2003; Marcelle,
2004; Bell and Figueiredo, 2012, Figueiredo, 2017; Hansen and Lema, 2019).
Internal technological learning processes comprise in-house effort and activities that take place by engaging in systematic and continuous improvements in production efficiency in new products and human capital development (Figueiredo, 2002, 2003). For instance, contributions to arranged experimentation in new ventures might require learning by changing through the modification of equipment and machinery, especially if this is built successively on the aggregated experience acquired in various activities. Kim (2001) argued that the technological learning process is proactively drawn by concentrated efforts to outline and direct how knowledge will be accessed and incorporated into the firm. The other example of in-house learning only occurs if there are practical problem-solving efforts in specific
39 projects in the form of trial and error learning. There are different types of the internal technological learning programme, both course-based and on-the-job training for employees and managers, as well as learning by training for selected staff. New knowledge creation is also driven by learning by searching activities through specialized in-house efforts in company laboratories, private R&D, and quality and control departments (Figueiredo, 2003;
Hansen and Ockwell 2014).
On the other hand, external technological learning involves acquiring knowledge skills and other elements of capability from external sources. Learning processes are promoted by the dyadic associations between enterprises and different types of external factors, for example, collaboration with universities or academic institutions and other external organizations.
These dyadic associations can be established with foreign companies through technology collaboration, strategic alliances, licensing agreements, and other forms of IOC, which transcend the local economy. By access, assimilation, and possible improvement of foreign technologies, such as trans-national IOC, may provide more enormous advantages in obtaining the important sources of learning by interacting with international companies, more technologically advanced partners (Hobday, 1994; Wong, 1999). Inter-organizational learning also takes place when enterprises collaborate with local competitors either through formalized ventures likes project partnering or non-formal channels such as learning by imitation and copying and domestic labour turnover. Such knowledge transfer across enterprises is an essential source of external learning in emerging economies (Bell and
Figueiredo, 2012; Figueiredo, 2017).
Bell and Figueiredo (2012) and Hansen and Ockwell (2014) strongly argue that both internal effort and interaction with external organizations are crucial for TC building of individual
40 firms (e.g. see Figueiredo, 2017; Tumelero et al., 2018; Hansen and Lema, 2019). However, intra-firm learning or human capital development within firms has received more attention in the literature of emerging economies than external technological learning, which involves organizational interaction. To balance this and to deepen our understanding of external learning, this research focuses on IOC as a major source for TC building in emerging economies.
2.4 Why does Inter-Organizational Collaboration matter in emerging economies?
A large body of literature on emerging economies agrees that firms are “rarely able” or “not able” to develop TCs on their own. Lall (1993b, 1995) and Wignaraja (1998), for example, both stated that manufacturing firms do not develop TC in isolation; or “the process is rife with externalities and interlinkages” (Lall, 2000: 19). Organizational interaction or collaboration between firms and external institutions is essential for increasing the knowledge base and TC building of a firm. Lall (1993b) stated that the promotion of linkages and the development of institutions to undertake activities beyond the scope of individual firms becomes a vital part of TC development. Indeed, TC is created as a result of the intense interaction between firms and other organizations (Bell and Pavitt, 1993, 1995; Wignaraja,
1998). The evolutionary theory highlights the role of research in enabling firms to develop new combinations of knowledge and pursue new technological paths (Nelson and Winter,
1982; Metcalfe, 1994). It has long been recognized that an important part of the research is access to external knowledge through collaboration with other organizations (Rothwell et al.,
1974; von Hippel, 1976; Tumelero et al., 2018).
However, those interactions mostly take place locally in emerging economies. According to
Lall (1995, 2000) and Bell and Pavitt (1993, 1995), local interaction is complex and more
41 important than international relationships in TC building. This research, therefore, focuses on local organizational interaction, which involves suppliers of capital goods, competitors, customers, consultants, technology suppliers, universities, industry associations, training institutions, and government or public research institutions (Lall 2000: 19-20; Bell and Pavitt
1993: 168; Wignaraja 1998: 36-37). These linkages help individual firms to deal with each other, to gain access to valuable information and facilities, and to create knowledge, skills, and standards that all firms need, but no individual firm will generate on its own (public goods).
The nature of IOC in emerging economies is far more dynamic and complex than in well- developed countries. The particular differences and problems in organizational interaction are discussed in the literature on national innovation systems. Innovation systems in developed countries are characterized by strong knowledge-exploitation and -exploration subsystems, and a high degree of interaction between organizations and robust institutional frameworks
(Freeman, 1987; Lundvall, 1992). On the other hand, in emerging economies innovation systems are usually characterized by a deficient socio-economic infrastructure, limited innovative capabilities, weak interactions between different organizations in the system and inadequate institutional frameworks supporting institutional settings (Arocena and Sutz,
2000; Cassiolato et al., 2003; Lundvall et al., 2009).
Their formal institutional, legitimate, and regulatory frameworks are largely weak, with unreliable enforcement mechanisms. There is a low level of co-operation between companies, and collaboration between different types of partners (private R&D, government research institutes, universities, technology-based firms) is rare (Arocena and Sutz, 2001). Recent studies with empirical evidence continue to show poor collaboration between firms and
42 external organizations, which reflecting the nature of the national innovation system and institutional structures of countries such as Taiwan (Hsu et al., 2009), Latin American countries (Crespi and Zuniga, 2012) and Malaysia (Chandran et al., 2014). Nevertheless, firms in these countries are making significant efforts to improve their IOC in order to acquire knowledge and resources for TC building and innovation activities.
In sum, TC building efforts in emerging economies are not linear, sequential, and straightforward process, nor are they guaranteed to succeed without sustained, purposive co- ordination, and organizational interaction. From the in-depth literature review, clearly can be noticed that IOC plays an important role in TC building for individual firms, although several limitations to our understanding remain (Lall, 1992; Bell and Pavitt, 1995; Wignaraja, 1998;
Molina-Domene, 2012; Bell and Figueiredo, 2012; Tumelero et al., 2018; Hansen and Lema,
2019).
2.4.1 Previous empirical research
There has been considerable interest in innovation collaboration in developed economies, which is spreading to developing economies. Previous empirical research has concentrated on two major issues: the determinants or motives for IOC; and the relationship between IOC and different innovation activities and performance. However, very few studies have addressed the effects of IOC on TC building. The following paragraphs discuss existing empirical work on innovation surveys in both developed and emerging economies.
2.4.1.1 Determinants of collaboration
In examining the rationale for co-operating with external partners with respect to R&D,
Cassiman and Veugelers (2002), using Belgian (CIS-1) 1993, found that access to external
43 knowledge and preventing knowledge leakage are the main determinants for forming co- operative R&D agreements. While co-operation with research institutions and universities leads to increased access to knowledge from the external environment, collaboration with suppliers and customers helps to protect their own knowledge or prevent knowledge leakage to competitors firms. Tether (2002), using UK CIS data 1997, found that higher R&D expenditure was beneficial for all types of collaboration partners, while firms’ size (larger firms) had a positive impact only in co-operation with universities and suppliers. Sharing costs and risks was another important reason for UK firms to collaborate.
Miotti and Sachwald (2003) also distinguished among different types of co-operative partners. Analyzing French CIS-2 from 1997, they found that technological frontiers were key determinants for collaboration. Vertical collaboration with suppliers and customers was important for complementary knowledge and market-related resources. Interestingly, collaboration with competitors was quite rare, with firms mainly co-operating to reduce R&D costs and risk, particularly those from high-technology industries.
Veugelers and Cassiman (2005), using the same CIS-1 Belgian data, found that high-tech firms are more actively involved in co-operative agreements with universities and research centers than are medium- and low-tech firms, where their ability to control or protect their own knowledge is not crucial. Sung and Carlsson (2007) also had similar results, that high- tech enterprises are more innovative than low-tech ones in product innovation, but not in product improvement or process innovation. Their study was based on the Korean Innovation
Survey (KIS) and examined the role of inter-firm networks and technological opportunities in performing innovative activities (product innovation, product improvement, and process innovation) of 1,124 high- and low-tech firms. They found that the determinants of firms’
44 innovative activities differed according to both innovation type and technological opportunity and that the network effects also differed according to both innovation type and technological opportunity.
Belderbos et al. (2004), using Netherlands CIS for 1996 and 1998 for both manufacturing and service firms, examined the determinants of innovative firms’ decision to become involved in
R&D co-operation; they differentiated between horizontal co-operation (with competitors), vertical co-operation (with suppliers and customers) and institutional co-operation (with universities and research institutes). The effect of firm size was more significant when firms co-operated with universities and research institutes, as market uncertainty was less considerable. In contrast, market uncertainty was substantial in co-operation with competitors and suppliers. Access to external knowledge was important for collaboration in all three cases, given that the resources channeled from research organizations.
Arranz and Arroyabe (2008), using Spanish CIS-2, found that firm size had a negative relationship on collaboration with universities and academic institutions, because smaller firms (SMEs) have limited resources, and the need for technological knowledge, in particular, encouraged them to co-operate with universities. Further, while some firms chose to co- operate with suppliers and customers more widely, others worked with research institutions to offset the high costs of innovation process. Similar results were found by Chandran et al.
(2014), who noted that SMEs tended to collaborate with universities and government research institutions more than did large firms. This study examines the level of university- industry collaboration (from both directions) of manufacturing firms and data from the
Malaysian National Innovation Survey from 1997 to 2008 (MNIS-3, MNIS-4, and MNIS-5).
Collaborative activities between universities and industry were relatively weak, as
45 manufacturing firms preferred closer relationships with customers, suppliers, and technical service providers.
Hsu et al. (2009) found that the NIS of Taiwan has two weak collaborative relationships with external organizations, first with universities and institutions and secondly with consumers, competitors, and suppliers. The reason for collaborating with customers was to reduce risk related to innovation activities; only large firms tended to collaborate with universities and research institutions. These results reflect the nature of industrial network vitality within
Taiwan’s Innovation System.
2.4.1.2 The relationship between IOC and innovation activities or performance
The second research issue, focusing on IOC for innovation with different organizations, reveals different effects on innovation activities or performance. Faems et al. (2005) found that university collaboration had a significant relationship with sales of innovative products that were new to the market and that other collaboration partners had a positive relationship on sales of innovative products that were new to the firm (but not new to the market). In contrast, R&D collaboration with rival companies had a negative association with sales of both types of innovative product (new to the firm and market). Tether (2002) found firms engaging in R&D collaboration tended to produce radical innovation (products that new to the market) rather than incremental innovation (products that new to the firm).
Belderbos et al. (2004b) analyzed the effects of R&D collaboration on the performance
(labour productivity and productivity in innovative sales) of manufacturing and service firms using Dutch CIS 1996 and 1998. The results show that R&D co-operation with universities and competitors had positive associations with the growth of sales of radical innovations,
46 products that new to the market. In contrast, co-operation with suppliers and competitors had a significant relationship with the growth of value-added per employee. Miotti and Sachwald
(2003) found that vertical co-operation with suppliers and customers, but not with competitors, showed a significant relationship with the share of innovative products, and collaboration with public institutions increased the number of patents issued to the firm.
A study in Malaysia investigated the relationship between innovation, productivity, and trade intensity (exports) of manufacturing firms using data MNIS-4 from 2002 to 2004 (Lee,
2008), concluding that these relationships are complex. Exports and industry’s technological characteristics have a significant effect on the decision to take on R&D, but no association with R&D expenditure. Firm size, exports, and local ownership have significant impact on both product and process innovation activities.
Aschhoff and Schmidt (2008)’s explored the effects of R&D collaboration on firm’s direct innovation performance using German CIS data from 2004-2005. They found that co- operation with competitors was positively associated with cost reduction factor to innovation activities, while collaboration with research institutes was positively associated with products new to the market; co-operation with universities and research organizations produce a higher share of market turnover than firms co-operating with customers and suppliers. Moreover, they find that the collaboration with suppliers and customers does not have any relationship with process innovations and sales of product imitations. However, Iammarino et al. (2012) found IOC for innovation with local and international partners had a positive relationship with technological competences and capabilities of firms based on a sample of 16,445 firms from the UK (CIS 4). The results show that local and international co-operation with other enterprises, suppliers, and clients, and local collaboration with universities were significant at
47 the 1% level. In contrast, both local and international co-operation with consultants and international co-operation with universities had a negative relationship.
A study by Crespi and Zuniga (2012) on six Latin American countries (Argentina, Uruguay,
Costa Rica, Panama, Colombia, and Chile) using firm-level data from innovation surveys explored the determinants of technological innovation and its effects on labour productivity.
The results showed that for half of these countries, IOC for innovation with foreign ownership and exporting was more likely to result in investment in innovation activities and encourage innovation investment. Scientific and market sources of information had almost no impact on firms’ innovation efforts, emphasizing the weak nature of the collaboration relationship characterizing the nature of national innovation systems in six countries.
Lee (2012) investigated the relationship between knowledge flows, organization, and innovation, using MNIS-4, finding that the relationship between technological innovation and knowledge flows was essential to innovation activities such as the acquisition of equipment, machinery, and software. It was also found that weak, globalization-related variables such as foreign direct investment and exporting affected certain types of innovation activities such as training and acquisition of equipment, machinery, and software. Lee’s research identified that firm-level organizational measurement and innovation activities were related to internal and external knowledge and information. By contrast, the collaborative relationship between innovative firms in Malaysia and foreign firms for innovation was relatively weak.
48 2.5 Research Gap
The TC of emerging economies is recognized as one of the crucial resources that providing sustainable competitive advantages for firms (Lall, 1992, 1994, 2000; Bell and Pavitt, 1993;
Kim, 1997, 2000; Bell and Figueiredo, 2012; Hansen and Ockwell 2014; Figueiredo, 2017;
Hansen and Lema, 2019). Technical advancement is a requisite driving force of economic and social development (Nelson, 1987; Wignaraja, 1998; Pietrobelli, 1998; Chuang, 2016).
Furthermore, technology and TC have become a fundamental element of competition in the international market. The growth, assimilation, and further development of the latest technology are determined by the patterns of co-operation, competition, and trade at the global level (Lall, 1992, 2000). The capacity to access and invent new technology thus affects the ability of firms in emerging economies to develop an indigenous TC and survive in world markets (Lall, 1992; Bell and Pavitt, 1993, 1995). Hence, TC has become the center of attention not only among researchers, but also among corporate figures and policy-makers
(see Hobday, 1995; Kim, 1997; Wong, 1999; Molina-Domene, 2012; Chuang, 2016).
Industrial development is acknowledged as a process of building up TC and transfiguring them into product and process inventions corresponding to continuous technical and technology upgrading (Kim et al., 2000; Lall, 2000; Miozzo and Walsh, 2006; Kiamehr et al.,
2013). In other words, industrial development is about the process of developing TC. Hence,
TC plays an essential role in building up the competitive advantage of an organization, sector, and also the country, into the future (Lall, 1992; Wignaraja, 1998). For example, the economic performance of South-East Asian countries, especially the Newly Industrializing
Countries (NICs) or four Asian Tigers, Hong Kong, Singapore, South Korea, and Taiwan, have developed relatively stable technological capabilities to accommodate domestic and international standards, a key determinant of their rapid economic development and
49 technological advance (Hobday, 1994, 1995; Pietrobelli, 1998; Wignaraja, 2001; Tsai, 2004).
Thus, the development of TC is vital, especially among manufacturing firms in emerging economies like Malaysia, attempting to catch up with the industrialized nations.
The question of how firms build or develop TC has attracted significant attention from innovation studies since the 1980s. Although it has been studied in a large body of literature
(e.g. Lall, 1992, 2000; Bell and Pavitt, 1993, 1995; Kim, 1997, 2000; Wong, 1999), TC building in emerging economies is still not clearly understood (Katz, 1984, Lall, 1992;
Wignaraja, 1998; Molina-Domene, 2012; Hansen and Ockwell, 2014; Chuang, 2016;
Figueiredo, 2017) and more recently by Hansen and Lema, (2019). This is partly because the nature of TC building in emerging economies is complex and challenging, and also because of the fast-changing technology frontiers. Technological knowledge is not shared equally among firms, nor is it readily imitated by or transferred across enterprises or industries like a physical product, because of its sizeable tacit element (e.g. Nelson and Winter, 1982; Dosi,
1988; Wignaraja, 1998; Miozzo and Walsh, 2006). There is little doubt that as a description of reality, in developed or emerging countries, evolutionary theory is far more realistic than neoclassical theory (production function approach) in understanding the nature of TC building in emerging economies. The TC literature indicates that technical and technological development is an outcome of the greater investment of a firm; therefore, the transfer and diffusion of information and technical knowledge are important elements in TC development.
The processes through which firms acquire skills and knowledge are often referred to as the technological learning discussed in Section 2.3.1 (Bell, 1984; Kim, 1997; Bell and
Figueiredo, 2012; Hansen and Ockwell, 2014).
50 The literature on TC building in emerging economies pays insufficient attention to the evolutionary theory of firm-level external technological learning, especially learning by interacting or collaboration with external partners (IOC). The evolutionary approach to technical change considers TC as the outcome of in-house technological competences and complex interactions among individuals, firms, and organizations within a specific socio- economic and institutional environment (Katila and Ahuja, 2002; Laursen and Salter, 2006;
Iammarino et al., 2012). This evolutionary perspective on organizational interaction with external partners mainly deals with issues relating to firms in developed countries (see e.g.
Laursen and Salter, 2006; Nieto and Santamaría, 2007; Iammarino et al., 2012; Miozzo et al.,
2016). According to Bell and Pavitt (1995: 87), although investment and post-investment play a significant role in TC building, organizational interaction with other organizations is also critical to individual firms for TC building in emerging economies. Bell and Figueiredo
(2012: 23) argued that there are numerous limitations to understanding technological learning and capability building in emerging economies, concerning the relative importance of different learning mechanisms, and more explicitly learning by interacting (also refer Hansen and Ockwell, 2014; Hansen and Lema, 2019). Consequently, the manner and extent to which these economies catch up with or overtake global leaders is rooted in the manner in which they engage in deliberate efforts to build up, use and manage the various learning mechanisms within their boundaries, and in partnerships with external organizations (e.g. buyers, producers, suppliers, users, universities, R&D institutes, and specialized engineering and consulting firms) (see Bell and Pavitt, 1993; Wignaraja, 1998; Bell and Figueiredo,
2012). The literature on technological learning strongly suggests that inter-organizational learning (formal and informal collaboration) is vital for their firm-level TC building (Kim,
1997, 2000; Marcelle, 2004, 2012). Many studies have proposed that human capital development and in-house efforts (learning within the firm) are important for TC building in
51 emerging economies, and studies focus on IOC, which is still relatively undeveloped in emerging economies context.
As explained in Section 2.4, a large body of literature on emerging economies agrees that firms are “rarely able” or “not able” to develop TCs on their own. Although several studies recognize that external technological learning or IOC are important in increasing the knowledge base and TC building of firms in emerging economies. However, very few in- depth studies, especially quantitative ones (using large data-sets such as innovation survey data), used to examine whether IOC for innovation leads to TC building. Bell and Figueiredo
(2012: 36) claim that the majority of these studies on technological learning and capability building are based on qualitative designs. A critical analysis of the technological learning and capability building literature clearly shows that IOC is an important learning strategy for firms in emerging economies that enable them to develop TC. The IOC literature sheds new light on the approach to TC building in emerging economies. Previous empirical research on
IOC literature mainly deals with two major issues: the first is about the determinants for IOC or the motives behind such collaborations. The second relates to the relationship between
IOC and different innovation activities/performance, such as products and services innovation, process innovation, labour productivity, trade intensity (exports), and product imitations. Nevertheless, the issues of technological learning and technological efforts, especially learning by interacting with external partners concerning to TC building at the firm-level, remain uncovered.
For a broader understanding of this issue, this research examines the influence of IOC on TC building in emerging economies, primarily focus on Malaysian manufacturing firms. Given the limited number of studies examining this relationship in a systematic way via the
52 quantitative method, especially using large datasets (such as innovation survey data). The main research question of this research will examine to what extent formal IOC affects
TC building in emerging economies. In particular, the study will consider the relationship between IOC-breadth and IOC-depth with different partners and their impact on TC building. Therefore, a mixed-method approach was employed in this thesis using quantitative (manufacturing firm-level data from sixth series MNSI) and followed by qualitative (interviews and three case studies) to investigate the above research questions
(more details refer to Chapter 4). The next chapter presents the conceptual framework and development of the research hypotheses for this research.
53 CHAPTER 3: INTER-ORGANIZATIONAL COLLABORATION AND TECHNOLOGICAL CAPABILITY BUILDING
3.1 Introduction
Chapter 2 discussed the literature review and identified the research gap. This chapter extends the previous chapter by discussing the theoretical foundation for this thesis. The discussion presents the basis upon which the conceptual framework is derived. This chapter develops our understanding of the concepts of IOC and TC building in emerging economies.
Section 3.2 focus on why firms in emerging economies engage in IOC, and Section 3.2.1 describes the concepts of IOC-breadth and -depth. Section 3.3 considers the conceptual framework. Section 3.4 discusses research hypotheses: Section 3.4.1 IOC-breadth and -depth and TC building (hypotheses 1-3), and Section 3.4.2 examines the impact of different organizational partners and proposes hypotheses 4-10.
3.2 Why do firms in emerging economies engage in IOC?
The nature of firms in emerging economies makes it difficult for them to innovate and develop capabilities (Lall, 1992, 2000; Wignaraja, 1998; Pietrobelli, 1998; Molina-Domene,
2012). Firms in these economies, often operate within a weak NIS, have inadequate resources, and are financially not strong against other constraints. IOC is the best possible strategy that firms can adopt to overcome these weaknesses and improve innovation and capability (Lee et al., 2001; Temel et al., 2013). Of the many reasons why firms engage in
IOC for innovation, the literature on emerging economies stresses three main reasons: (1) to access to external knowledge and resources; (2) to reduce or share the cost-risk; and (3) for problem-solving (e.g. see Dodgson, 1993; Lall, 1992; Teece et al., 1997).
54 First, IOC for innovation is crucial for firms in emerging economies to access valuable or specialized external knowledge and resources, including complementary, supplementary, or similar knowledge from co-operation partners (Powell et al., 1996; Tether, 2002;
Chesbrough, 2006). The major motive for firms to engage in collaboration with different partners is to have access to various types of knowledge; for example, co-operation with customers and suppliers for product knowledge or experience of consumers (von Hippel,
1988; Tether, 2002; Belderbos et al., 2004; Nieto and Santamarıía, 2007), with consultants and private research organizations to find solutions to problems (Belderbos et al., 2004;
Tether and Tajar, 2008; Sánchez-González, 2014; Srinivasan, 2014), with competitors to obtain similar knowledge or technology (Hamel et al., 1989; Veugelers and Cassiman, 2005;
Chen et al., 2015), and with universities and government research institutions for specialized knowledge or generic technologies (Cassiman and Veugelers, 2002; Tether, 2002; Miotti and
Sachwald, 2003; Temel et al., 2013). Firms need external knowledge in order to understand the nature and value of both tangible and intangible assets, which are the foundation of the productive capacity in becoming knowledge-based organizations. Focusing only on internal sources of knowledge is not sufficient for firms to expand their innovation activities or build capabilities; therefore, co-operation with external partners is crucial to obtaining new knowledge and information (Tether, 2002; Belderbos et al., 2004). Lee et al. (2013) argued that acquiring knowledge from the external environment is far more important than exploiting internal knowledge for firms in emerging economies, because firms that are able to access new information or knowledge may bring novelty and commercial value (see also
Chen et al., 2015). Collaboration with different partners enables firms to exchange information, make improvements to their services, help each other do a better job, and also share the knowledge among employees, improving the competencies of the firm.
55 Secondly, the most common motives of firms in emerging economies to engage in IOC for innovation in recent years has been to spread the costs and risks of innovation activities (e.g.
Lee et al., 2001; Pei et al., 2012; Guo et al., 2015). Unlike firms in developed countries, firms in emerging economies tend to lack financial resources or capital for undertaking large projects. These challenges are even more critical to small and medium size enterprises
(SMEs) in emerging economies (Hsu et al., 2013; Vrgovic et al., 2012). The significant challenges for SMEs to innovative or capabilities development relate to the shortage of resources in terms of technological knowledge, infrastructure, finance and human capital that together constrain the scope of innovation activity. In emerging economies, GDP growth largely depends on small and medium-sized enterprises (Vrgovic et al. 2012; Dooley et al.
2017). For example, according to the latest SME report (SME Corporation Malaysia, 2018), around 98.5% of business establishments in Malaysia are SMEs. The SMEs are vastly distinctive from large firms or MNCs in that most of them lack a formal process for developing new products or technologies (Nieto & Santamaria 2010; Chandran et al. 2014)2.
Guo et al. 2015 argued that SMEs relatively suffer more from resource constraints, and they have to the trade-off between search depth and search breadth for innovation 3 . Larger emerging economies like China, Brazil and India that usually contain big transnational companies, so SMEs have greater opportunities to build networks with big companies to enhance their innovation activities. However, small emerging economies (such as Malaysia,
Thailand) may lack the presence of many transnational companies and therefore cannot have their innovative efforts jumpstarted this way. These economies must find other partners and methods to initiate innovation, preferably through collaborative networks.
2 Similarly, Malaysia SMEs also faced related challenges (e.g see SME Corporation Malaysia 2006, 2014, 2018). 3 Large firms have comparatively adequate slack to support their pursuit of search depth and search breadth concurrently. They are more proficient of managing the complexity of external search from a wide range of knowledge sources compared to SMEs (Guo et al. 2015).
56 Larger innovation projects or new technology development require colossal capital and involves considerable level of risks (e.g. pharmaceuticals and electronics projects), and not all the firms have the capacity to deal independently (Kim, 1997; Hobday, 1994). Dodgson
(1993: 234) argued that new technology building is exceptionally costly; for example, even then, drug or microprocessor development could cost over $200 million and new telephone projects around $1 billion. Technological co-operation agreements with larger firms and
MNC make it much easier for SMEs to develop TC or new technology, especially firms in emerging economies (Hobday, 1994, 1995). That is, IOC provides greater opportunities for firms in emerging economies to engage in larger joint projects and to bring down the costs and risks associated with them, particularly in innovation and capabilities development.
Lastly, problem-solving is a key reason for firms to engage in collaboration activities. In general, firms will face various problems and challenges in innovation activities, specifically in existing or new products or services and process development (e.g. Katila and Ahuja,
2002; Tether, 2002; Belderbos et al., 2004b). These problems are frequently related to the installation of new hardware/software, or operating new machines (Pei et al., 2012). For a firm facing these problems, collaboration with suppliers or consultants may provide the best solution to installing the hardware or operating the new machine effectively. Katila and
Ahuja (2002) and Laursen and Salter (2006) provide empirical evidence that searching for external knowledge and innovative ideas can contribute to and improve a firm’s capacity to innovate and solve problems. Katila and Ahuja (2002: 1184) stated that product search refers to an “organization’s problem-solving activities that involve the creation and recombination of technological ideas”. Firms in emerging economies rarely have sufficient information and experience to manage or find solutions to the problems or challenges in a highly competitive environment. As they gain from past experience from other organizations, they have a better
57 chance of solving a particular problem and can have standardized solutions (Powell et al.,
1996). Therefore, shared experiences lead to the development of a routine approach and enable them to generate new resources more efficiently, and crucial for finding solutions or responses to the new situations.
In sum, IOC for innovation with customers, suppliers, competitors, consultants, private R&D institutes, universities and government research institutions is significantly important for firms in emerging economies to access external knowledge and resources, reduce or share the cost-risk, and problem-solving. These seven organizational sources are key actors that enhanced innovation and capability of a firm (e.g. Laursen and Salter, 2014; Iammarino et al.,
2012; Lee, 2012; Crespi and Zuniga, 2012; Chandran et al., 2014; Miozzo et al., 2016). In relevant to this study, the seven organizational sources are used here in conjunction with
IOC-breadth and IOC-depth (both concepts are clearly explained in the following section and their measurement is described in Section 4.4.2.2). However, formal IOC, particularly IOC- breadth and IOC-depth, is an under-researched area in the context of emerging economies
(see Guo et al., 2015; Zhang, 2016). Therefore, this study focuses on IOC-breadth and IOC- depth, and the seven organizational sources listed above.
3.2.1 IOC breadth and depth
IOC-breadth and IOC-depth describe an essential strategy for firms to access external knowledge and resources, new technology, and market information to develop innovation capabilities (Guo et al., 2015; Cruz-González et al., 2015; Zhang, 2016). Both IOC-breadth and IOC-depth are widely used in open innovation, innovation management and policy literature, and organizational learning (e.g. Katila and Ahuja, 2002; Laursen and Salter, 2006;
Leiponen and Helfat, 2010).
58
On the first hand, IOC-breadth and IOC-depth are closely linked to the concepts of exploration and exploitation collaboration of organizational learning (March, 1991), and technological learning is the bridge between IOC and TC building in this dissertation.
Consequently, the literature on organizational learning and change through the adaptive collaboration for new technologies has characterized fundamentally differing collaboration strategies frequently, in terms of being explorative or exploitative. Exploration refers to firm behaviours characterized by search, variation, risk-taking, experimentation, play, discovery and innovation, while exploitation implies how firms enhance productivity and efficiency through refinement, choice, selection, implementation and execution (March, 1991:71).
Levinthal and March (1993: 105) added that exploration involves “a pursuit of new knowledge,” whereas exploitation involves “the use and development of things already known”. Following the learning viewpoint of Katila and Ahuja (2002), the search scope or breadth indicates how widely the firm searches for new knowledge, fitting the concepts of exploration; search depth, or how often firms re-use their existing knowledge, is closer to exploration. However, it is unclear whether this logic remains the same when the search phenomenon is analyzed from the point of view of formal external collaboration (IOC).
Researchers tend to agree that exploration collaboration (or breadth) is more important than exploitation (IOC-depth) in enhancing the knowledge pool of a firm (e.g. Laursen and Salter,
2014; Tippmann et al., 2014; Eriksson et al., 2016; Ferreras-Méndez et al., 2016).
Exploration collaboration involves seeking, assimilating, and applying new and distant knowledge, and hence a wider range of collaboration for information in distant domains to generate new combinations. It avoids relying on overlaps in existing knowledge models
(Tippmann et al., 2014). Daft and Weick (1984) argued that organizations that continuously
59 experiment, test, and try new behaviours based on this type of collaboration are essential for new and distant information. Exploring new and distant knowledge allows the firm to develop novel interpretations and knowledge combinations. Recall that IOC-breadth is the number of different organizational channels or external partners that firms rely upon for their innovative activities, while IOC-depth is the number of different organizational sources or co-operation channels that a firm draws upon deeply for innovative activities. The intensity of the collaboration relationship is the central point to both IOC-breadth and IOC-depth here
(Hsieh and Tidd, 2012; Cruz-González et al., 2015). Accordingly, IOC-breadth with external partners should be accepted as “shallow in nature” (Cruz-González et al., 2015: 35).
Similarly, Dittrich et al. (2007: 1498) and Granovetter (1973) refer to collaboration breadth or wider co-operation as “weak ties” when firms exhibit low participation in co-operative relationships with non-familiar partners. Even though this type of collaboration helps discover and access new (and valuable) sources of knowledge and resources from various external channels, but its superficial character only adds small improvement to a firm’s existing knowledge base. This is because the absorptive capacity of companies to acknowledge the importance of new external knowledge, assimilate it and make use of it as a function in related fields is difficult for the enterprise to understand, and consequently to gain from external knowledge in distant channels (Cohen and Levinthal, 1990). As the organization deepens into knowledge and information from external partners, this becomes more readily understandable, allowing firms to access valuable distant knowledge, which could not be acquired through a more superficial search (Hsieh and Tidd, 2012; Cruz-
González et al., 2015). In line with the above view, exploration collaboration (IOC-breadth) is defined as a situation where firms collaborate widely with a large number of organizational
60 channels or external partners for sources of knowledge and resources for their innovative activities. In short, IOC-breadth leads to distal experience (or breadth knowledge)4.
Two broader views are related to exploitation strategy: collaboration depth in the organizational learning literature. The first view, researchers have argued for the role of exploitation to increase the outcome from the firm’s co-operation activities for new innovations (Petel and Van der Here, 2010). According to this viewpoint, in order to survive, companies not only search for product or process innovation, but at the same time, they have to exploit new knowledge from costly exploration collaboration. Exploitative collaboration thereby relies on a replication strategy (Tippmann et al., 2014), in which co-operating members collaborate locally to draw on familiar and accumulated knowledge. Here, exploitation is a form of learning associated with building economies of scale and scope around recently introduced innovations (Petel and Van der Here, 2010; Eriksson et al., 2016).
The second view of exploitation treats the construct as a different type of learning that arises from a firm specializing in a particular domain or technology (Zang et al., 2014; Stanko and
Henard, 2017). This form of exploitation is seen as allowing firms to build up a more solid, deeper understanding of the knowledge and resources a firm already possesses. Therefore, we adhere to exploitation as being the learning from specialization in a knowledge domain or technology. In this view, exploitation-type learning is characterized by a degree of connectedness, although not in the structural sense (e.g. Sheremata, 2000), but in the sense of the cumulative development of knowledge within a known or proximate technological boundary, often aimed at making improvements over time. Thus in our theorizing, exploitation refers to learning by refinement and extension and the building and using of a critical mass of knowledge in a domain (Gupta et al., 2006; March, 1991). In line with the
4 Patel and Van der Have (2010) used the notions “proximal-exploitation/depth” and “distal-exploration/breadth” relative to the different dimensions of external search.
61 above, exploitation collaboration (IOC-depth) is when a firm collaborates narrowly, or the extent to which firms draw deeply on the sources of knowledge and resources from few organizational channels for innovative activities. IOC-depth is important in creating a proximal experience or depth knowledge.
On the other hand, in open innovation literature5, Laursen and Salter (2006) earlier study used external collaboration breadth and depth; collaboration breadth was the number of organizational channels or external sources of knowledge that firms rely upon for their innovative activities (Leiponen and Helfat, 2010; Laursen and Salter, 2014) and collaboration depth was defined as the extent to which firms draw deeply from the different organizational sources for innovative activities (Laursen and Salter, 2006). Collaboration breadth and depth are used to identify how openly or closely firms search for knowledge and information from the external environment for their innovation activities. Laursen and Salter (2006) developed breadth and depth concepts, following the seminal work by Katila and Ahuja (2002), who defined the search effort using two dimensions: search scope and search depth. Search scope describes how widely a firm searches for new knowledge or sources of information, and search depth refers to how deeply it reuses its existing knowledge (Katila and Ahuja, 2002:
1183). These search effort dimensions are viewed from an internal learning perspective. On the other hand, Laursen and Salter’s (2006) open search strategies of breadth and depth are seen from the perspective of external knowledge sources. In this study, the concept of breadth and depth are viewed from the external collaboration perspective (formal organizational collaboration), similar to Laursen and Salter’s (2014) study, which explores innovation collaboration breadth (formal collaboration breadth). However, researchers are largely unable to identify or define the formal collaboration depth variable due to the weakness of the
5 Since the seminal work by Chesbrough (2003), open search outside of the organizational boundaries has attracted significant attention in the open innovation literature (Chesbrough, 2006; Leiponen and Helfat, 2010; van Wijk et al., 2012; Zang et al., 2014).
62 innovation survey data6. In contrast, the Malaysian National Survey of Innovation (MNSI) has sufficient data to define both formal collaboration breadth and depth variables, which allow this study to examine both IOC-breadth and IOC-depth.
IOC-breadth describes when firms search or collaborate widely with a large number of organizational channels or external partners for sources of knowledge and resources for their innovative activities. Collaboration widely with different partners enhances the knowledge pool by adding distinctive new variations and expands the organization’s new product lines through enhancing re-combinatory search (Katila and Ahuja, 2002; Nelson and Winter,
1982). Similarly, March (1991) pointed out that new variations are crucial for providing sufficient solutions to problems and challenges. The evolutionary perspective perceives this effect as selection effect of variation, and limited sources of knowledge can be developed with the same set partners or search channels. An increase in IOC-breadth adds a new way of searching and improving the possibilities of identifying new sources of information and useful combinations for firms in emerging economies.
The second concept, IOC-depth, describes when firms collaborate or search narrowly, or the extent firms draw deeply on sources of knowledge and resources from different organizational channels for innovative activities. Innovation collaboration is not only searching for new knowledge with a wide number of channels; it also involves access to key information from deep within those sources (Laursen and Salter, 2006). For innovation activities, organizations often access the key knowledge heavily from a few numbers of external partners. Levinthal and March (1981) pointed out that repeated usage of the same knowledge elements or collaboration with the same partners will minimize the errors and
6 The major limitation of UK-CIS data is its inability to identify the depth of formal innovation collaboration. In this dissertation, the Malaysian National Survey of Innovation (MNSI) has a slight advantage over the UK-CIS in capturing the deep formal external innovation collaboration (IOC-depth).
63 false starts and enhanced the progress of routines, strengthening the co-operation activities.
Dittrich et al. (2007: 1498) and Krackhardt (1992) refer to IOC-depth as strong ties, characterized by intimate, recurrent, and trustful relationships with co-operating partners.
From IOC with different partners, every individual firm establishes a reliable path and pattern of interaction over time, which come from deeper understanding and the common ways of operating together from previous collaboration.
3.3 Conceptual Framework
The current experience of developing and emerging economies shows that TC building is a complex, challenging, and not linear process (e.g. see Lall, 1992; Wignaraja, 1998; Molina-
Domene, 2012). National innovation systems (NIS) in these economies’ are usually characterized by a deficient socio-economic infrastructure, limited technological capabilities, weak interactions between different organizations in the system and inadequate institutional frameworks supporting institutional settings (Arocena and Sutz, 2000; Cassiolato et al., 2003;
Lundvall et al., 2009). Firms these economies are characterized by deficient levels of absorptive capacity and high level of localization (Lall, 1992; Pietrobelli, 1998, Szogs, 2008).
Often firms face significant challenges relating to the shortage of knowledge and resources for TC development. As already established in Chapter 2, evolutionary theory is more realistic than the neoclassical theory (production function approach) in understanding the nature of TC building in emerging economies. The evolutionary perspective of technical development considers TC as a function of two separate but interrelated dimensions: (a) result of in-house technological competences and (b) intense collaboration with external partners within a specific socio-economic and institutional environment. Therefore the theoretical framework described here focuses on technological learning linking the IOC and
TC building of Malaysian manufacturing firms.
64 The evolutionary theory highlights the role of research in enabling firms to develop new combinations of knowledge and pursue new technological paths (Nelson and Winter, 1982;
Metcalfe, 1994). It has long been recognized as an important part of the research is access to external knowledge through collaboration with other organizations (Rothwell et al., 1974; von Hippel, 1976). Similarly, Nicholls-Nixon (1993) argued internal R&D and technology sourcing linkages are critical to building capabilities required in a new technological paradigm are dependent on firms’ absorptive capacity. The TC and IOC literature identified a strong link between external collaboration and TC building in emerging economies (e.g. see
Lall, 1992, 2000; Bell and Pavitt, 1993, 1995; Wignaraja, 1998; Chen et al., 2015). IOC is an important strategy, which firms can adopt to extend the knowledge search beyond boundaries to increase their existing knowledge and resources for TC development.
The literature on TC (e.g. Lall, 1992, 2000; Bell and Pavitt, 1995; Wignaraja, 1998) and technological learning (e.g. Kim, 1997, 2001; Figueiredo, 2003; Bell and Figueiredo, 2012;
Figueiredo, 2017; Hansen and Lema, 2019) in emerging economies agrees that firms are unable to build TC on their own; IOC is crucial to develop their external knowledge and resources for TC development. Lall (1993, 1995) and Wignaraja (1998) stated that manufacturing firms do not develop TC in isolation or “technological learning in a firm does not take place in isolation, the process is rife with externalities and interlinkages” (Lall, 2000:
19). The promotion of linkages and the development of institutions to undertake activities beyond the scope of individual firms becomes a vital part of TC development (Lall, 1993b).
Firms that actively participate in formal organizational collaboration are more capable of transferring valuable external knowledge into internal capabilities, and they are more knowledgeable in manage the relationship with co-operating partners (Miozzo et al., 2016b).
65 TC is created as a result of the intense interaction between firms and external organizational partners (Bell and Pavitt, 1993, 1995; Wignaraja, 1998).
The organizational learning suggests that the capability of a firm to renew or reconfigure technological capabilities are based on firms’ ability to develop new competencies by acquiring new knowledge from external sources and integrating or combining it with existing knowledge bases, which know as absorptive capacity (Teece et al. 1997; Cohen and
Levinthal, 1990; Ferreras-Méndez et al. 2016). Collaboration has a significant impact on the firm’s absorptive capacity as the literature has broadly acknowledged (Dantas et al. 2008;
Szogs, 2008; Kobarg et al. 2019). The network ties in which a firm is embedded influence its absorptive capacity through learning by interacting with external actors in the network. In organizational development literature, for instance, academics adopting a resource-based view (RBV) and knowledge-based view (KBV) have pointed out the importance of interaction with external channels for information and knowledge in emerging economies as a central element of TC development (Mathews, 2002). According to various empirical studies, a company’s external collaboration strategy within a technological trajectory can significantly influence its TC building (Powell, 1996; Katila, 2002; Katila and Ahuja, 2002), especially in emerging economies (Lall, 1992; Wignaraja, 1998), in Malaysia (Wong, 1999;
Hansen and Ockwell, 2014; Hansen and Lema, 2019).
National innovation systems (NIS) literature on emerging economies, the main research interest concerns technical development and the importance of knowledge flows from various local actors (Malerba and Mani, 2009; Lundvall et al., 2009). Bell and Figueiredo (2012) and
Hansen and Ockwell (2014) strongly argue that interactions with external organizations like universities, government research institutions and technological organizations are crucial for
66 technological learning by individual firms in emerging economies. Similarly, Kim (1997) argued that technological learning was an essential aspect of TC building in the Korean automobile industry. A significant advance in TC formation in latecomer firms is often related to learning through local networks and more advanced technology companies
(Mathews, 2002; Bell and Figueiredo, 2012). Bell and Albu (1999) strongly argued that local sources of learning are a critical component of local knowledge systems in emerging economies firms’ TC development. For firms in emerging economies, IOC with external partners is more crucial than in developed countries to encourage innovation and TC building
(catch-up frontier technology) (see e.g. Wignaraja, 1998; Pietrobelli, 1998; Shan and Jolly,
2013). It can be concluded that IOC is more critical for firms in emerging economies for TC building than for firms in developed countries.
To expand the understanding of the evolutionary theory on TC development related to intense collaboration (also known as linkage capabilities) with external partners. There is a scarcity of analytical framework from the evolutionary perspective on how and which external collaboration important for TC building in emerging economies context. Therefore, this thesis focus on (1) two collaboration strategies: IOC-breadth and IOC-depth, and (2) deeper collaboration with different external organizations (customers, suppliers, competitors, consultants, private R&D institutes, universities and government research institutions)7.
First, IOC-breadth and IOC-depth were discussed from both the exploration and exploitation and formal external collaboration perspectives above. The IOC strategies of a firm are strongly influenced by the richness of technological opportunities available in the
7 The literature identified seven external organizations: customers, suppliers, competitors, consultants, private R&D institutes, universities and government research institutions, are significantly crucial for firms in emerging economies to access external knowledge and resources, reduce or share the cost-risk, problem-solving, and also TC building. These seven organizational channels are used in this research to develop the concepts of IOC- breadth and IOC-depth following Laursen and Salter (2006) seminal work.
67 environment, and the way firms collaborate to build up the quantity and quality of knowledge and TC building in emerging economies. In manufacturing industries, with high levels of technological opportunities and extensive investments in search by other firms, a firm will often need to collaborate more widely and deeply in order to gain access to critical knowledge sources for technological success. From an absorptive capacity perspective, TC development and technological learning demand two elements of absorptive capacity: (1) existing knowledge base and (2) intensity of effort (Cohen and Levinthal, 1990; Kim 1997,
1998). The existing knowledge base (prior knowledge or past learning) and the intensity of effort or collaboration with external organizations (mechanisms for knowledge transfer) can be related to the concept of depth and breadth collaboration in this research.
The evolutionary theory refers to IOC-breadth as the diversity of the collaborative network: diverse sources of knowledge allow the firm to create new combinations of technologies and knowledge (Nelson and Winter, 1982; Metcalfe, 1994). Such variation or an increase in breadth collaboration adds a new way of searching and improving the possibilities of identifying new sources of information and useful combinations. On the other hand, this perspective perceives this effect as the selection effect of variation, and limited sources of knowledge can be developed with the same set partners or search channels. IOC-depth is the continuity of collaboration: the firm’s current innovation and technological capability are determined by its history and experience (Nelson and Winter, 1982; Dosi, 1988). The deeper collaboration increases the prior knowledge base or past learning experienced (absorptive capacity), which critical for firms TC development (e.g. see Katila and Ahuja, 2002;
Ferreras-Méndez et al. 2016; Kobarg et al. 2019).
68 A firm’s success or failure in local or international markets depends on how the firm changes its collaboration strategies in relevant current and future technological development to face competition. Lall (2000: 18) argued that different technologies could evolve from a wider range of breadth skill and knowledge, but also from a narrow range of specialization actors and others from a broader range of partners. Dittrich et al. (2007: 1498) and Iorio et al. (2017) refer to IOC-breadth and IOC-depth as weak and strong ties. A weak relationship is useful when a firm is exploring new technology and does not necessarily wish to enter a flexible form of relationship because of uncertainty about the new technology’s benefits them. In contrast, strong ties are critical in exploiting knowledge and making the most of established technologies and products; trustworthy and intensive co-operation with organizations is a prerequisite.
According to various empirical studies indicated that the character of a firm’s IOC within a technological trajectory could influence its TC building and innovative performance (Katila,
2002; Katila and Ahuja, 2002). In IOC, whether a firm collaborates widely or closely is strongly influenced by the richness of technological opportunities available in the environment and by the co-operation activities of other firms. Different technologies can also have varying degrees of dependence on interaction with outside sources of knowledge or information, such as other firms, consultants, capital goods suppliers, or technological institutions (Nelson and Winter, 1982; Levinthal and March, 1993). Therefore, both IOC- breadth and IOC-depth have an influence on TC building in emerging economies.
69
IOC-Breadth
Inter-Organizational Collaboration (IOC) TECHNOLOGICAL CAPABILITY (TC) IOC-Depth
Figure 3.1: Framework linking IOC: (Breadth & Depth) and TC.
Second, a firm’s choice of suitable IOC partners or organizations is essential in TC building
for any country. Evolutionary economists highlight the role of inter-organizational
collaboration for external choices in facilitating firms to identified from diversify sources and
enable the firms to develop new combinations of technologies and knowledge (Nelson and
Winter, 1982). For example, co-operation with customers and suppliers for product
knowledge or experience of consumers (von Hippel, 1988; Tether, 2002; Belderbos et al.,
2004; Nieto and Santamarıía, 2007), with consultants and private research organizations to
find solutions to problems (Belderbos et al., 2004; Tether and Tajar, 2008; Sánchez-
González, 2014; Srinivasan, 2014), with competitors to obtain similar knowledge or
technology (Hamel et al., 1989; Veugelers and Cassiman, 2005; Chen et al., 2015), and with
universities and government research institutions for specialized knowledge or generic
technologies (Cassiman and Veugelers, 2002; Tether, 2002; Miotti and Sachwald, 2003;
Temel et al., 2013).
Such diverse sources enable firms to select from different technological paths (Metcalfe
1994), and there are distinct differences among external organizations (e.g. in offering
complementary, supplementary or similar knowledge) in how they collaborate and what kind
of outcome they can accomplish (Nieto and Santamaría, 2007; Chen et al., 2015). The
70 distinct characteristics and objectives of each external organizational channel allow the firms
to access different knowledge or resources (e.g. complementary, supplementary or similar
knowledge), which lead to different results. Instead, the relationship between IOC-depth and
TC building in emerging economies may be depending upon on different external
organizational channels. Figure 3.2 represents the proposed conceptual framework of this
thesis, which will be validated in the quantitative and qualitative phases (Chapter 6) and case
studies analysis (Chapter 7).
Inter-Organizational Collaboration: H1
Customers H4 B R H5 E Suppliers A H6 D Competitors T TECHNOLOGICAL H H7 CAPABILITY Consultants
H8 Private R&D Institute D E H9 Universities P T H Government Research H10 Institutions
H2
H3 (IOC-Depth more important IOC-Breadth)
Figure 3.2: Conceptual Framework.
71 3.4 Research Hypotheses
Following the conceptual framework (Figure 3.2) discussed in section 3.3, this section formulates the hypotheses that will be examined in this study. The hypotheses grouped into two categories. The first category addresses the link between IOC: breadth and depth, and TC building. The second category explores the interrelationship between seven organizational partners (IOC-depth) and TC building.
3.4.1 Hypotheses relating to IOC: Breadth & Depth and TC building (H1 – H3). 3.4.1.1 IOC-breadth and TC Firms may co-operate widely (IOC-breadth) with a large number of organizational channels to access new or diversify sources of knowledge and resources for their innovative activities.
In order to cope with technological advancement, they form relationships with different actors to access new technical information and develop competencies and capabilities for their long-term survival. Often previous research has overlooked the importance of new collaboration relationships and new knowledge from the external environment in TC development in emerging economies (e.g. Katz, 1984; Bell and Pavitt, 1995; Molina-Domene and Pietrobelli, 2012). Narrow or intense collaboration is not always beneficial, and for new and diverse technological knowledge firms must co-operate widely with new partners.
Ferreras-Méndez et al. (2016) stated that intense co-operation may limit the growth of the firm’s business network if limited knowledge and resources are available from current partners. Conversely, greater innovation successes mainly come from a wider range of knowledge searching in a variety of technological domains (Laursen and Salter, 2014; Cruz-
González et al., 2015).
First, IOC-breadth with different partners adds distinctive new variations to existing knowledge and expands the organization’s new products line through enhancing re-
72 combinatory search (Katila and Ahuja, 2002; Nelson and Winter, 1982). The likelihood of technological success is highly uncertain in emerging economies context, firms often lack of key knowledge for TC building, especially tacit knowledge (Chen et al., 2015). Both tacit and explicit knowledge are critical for technical development, but tacit knowledge is very difficult to access or transfer from one firm to another. Wider co-operation with different partners increases the possibility of acquiring tacit knowledge from co-operating organizations. Lodh and Battaggion (2014) argued that some technological knowledge is tacit in nature, and wider co-operation is an effective strategic choice in acquiring it from external parties. Similarly, access to tacit knowledge from outside is crucial for increasing the collaboration breadth, and the exploration of new technologies and capabilities development
(Laursen, 2012; Miozzo et al., 2016).
Second, expanding collaborative networks widely (possibly with new actors) increases the likelihood of accessing knowledge that does not exist internally. In technology-based industries, co-operating with new actors provide greater opportunities for firms to access new technological knowledge that not available within existing or internal resources pool (Ahuja,
2000; Grant and Baden-Fuller, 2004). For instance, existing pharmaceutical companies have built their biotechnological knowledge base from co-operation with universities and new biotech companies, and forming relationship with new partners have high possibility for new technology development which firms sometime cannot get from existing partners (Powell et al., 1996; Zhang and Baden-Fuller, 2010). In contrast, when a firm is exploring new technology it may not necessarily enter flexible forms of relationship due to uncertainty of the outcome of the technology’s development in the short or the long term (Dittrich et al.,
2007; Granovetter, 1973). In other word, could said as safe play because of firm could avoid relationship with existing partners (protect their reputation), if those co-operation not doing
73 well. Thus, Laursen and Salter (2006, 20014) explain that firms acquire key sources of information from external partners by trial and error techniques, although this requires much energy and time. Lall (2000: 18) argued that different technologies in emerging economies can acquire a wider range of breadth skills and knowledge from interaction with consultants and suppliers.
Third, IOC-breadth not only offers new or diverse resources, but at the same time provides greater flexibility in reducing risk in the uncertain technological and market environment found in emerging economies (Lamin and Dunlap, 2011; Belderbos et al., 2011). According to Duysters and Lokshin, (2011) breadth collaboration offer flexibility to deal with risk and uncertain in technological environment and find that border co-operation network are associated with strong technology innovation. The literature on collaboration suggests that firms can use a radar function by linking with various organizations and accessing novel information in a world which is dynamic and lacks transparency (Faems et al., 2005).
Belderbos et al. (2011) refers to cherry picking when a firm collaborates with the most desired external organizations. The wider co-operation increases the flexibility of the firm to identify the right or the best partners that could bring more novelty to the firm. In other words, by accessing the number of collaboration, the firm improves the probability of obtaining key knowledge that will lead to a valuable outcome or technological development. IOC-breadth is thus important for adding distinctive new variations, acquiring knowledge that did not exist internally and reducing risk related to technology and markets that is critical for firms in emerging economies to build TC. In the light of this, the following hypothesis is therefore proposed:
Hypothesis 1: Inter-organizational collaboration breadth has a positive relationship/association with technological capability building.
74 3.4.1.2 IOC-depth and TC
The other form of collaboration, firms draw deeply the key source of knowledge from familiar organizational partners with a particular technological or application domain. IOC- depth provides different dimensions in knowledge searching from external sources (formal
IOC). IOC-breadth (or co-operate widely) may increase the quantity and diversity of knowledge from external partners, but for stronger TC building IOC-depth and experience knowledge are required. A close relationship with a limited number of partners enables access to in-depth knowledge from few specialized technological areas (Hamel and Prahalad,
1994; Kim, 1999). As Laursen and Salter (2006:136) suggest, “assessing the depth of a firm’s contacts with different external organizations provides a mechanism for understanding the way firms collaborate deeply within the innovation system and how these external sources are integrated into internal innovative efforts”. The following paragraphs discuss the importance of IOC-depth in relation to TC building.
First, intense learning from collaborating firms increases the access to key knowledge, which enhances the process of technological accumulation and capability building. Intense co- operation with external organizations (like technological enterprises) are crucial elements for organizational learning (specifically for long-term learning) in emerging economies (Kim,
1999; Marcelle, 2004). Since, organizational and technological learning are critical component of TC development in emerging economies. Intense learning from co-operating partner is important to access key technological resources and specialized knowledge that can facilitate the technological learning and TC building of firms in emerging economies at the organizational level (Kim 1999). Dittrich et al. (2007) argued that strong ties are critical in exploiting knowledge for TC development, requiring trustworthy and intensive learning activities with co-operating organizations (Krackhardt, 1992). Working with the same
75 partners frequently increases the learning experience of managers; co-operation activities are more predictable, as the information is familiar and product and process development are better understood.
IOC-depth (exploitation learning) with downstream actors not only brings commercial benefits but also access and learn more about tacit knowledge (Grant and Baden-Fuller, 2004;
Zhang, 2016). For instance, in 2013 Mitsubishi and Renault-Nissan formed collaboration;
Mitsubishi developed two new Renault-based models to increase their market share in the
United States (Zhang, 2016). Exploitation learning requires in-depth knowledge transfer and leads to economies of scale (Dittrich et al., 2007). This is only possible when firms have intense interaction with co-operating partners than in weak ties, because only strong ties with the requisite intensity for technology development. Nylen (2007) and Hsieh and Tidd (2012) argued that more complex or iterative task development (like technology) demands more intense co-operation, while breadth collaboration is only capable of dealing with simple and sequential technical development. Leiponen and Helfat (2010) argued that too much breadth collaboration pushes firms to encounter higher marginal costs due to increased complexity of managing both the diversity of knowledge and maintaining the relationships with many partners. The benefits to technological knowledge of recombination from different organizational channels may diminish as the number of partners increases (Leiponen and
Helfat, 2010; Laursen and Salter, 2014). For instance, the NK model8 (Kauffman, 1993) reveals that the number of interactions increases with components, so it becomes more difficult for firms to assimilate and develop new technological components (Leiponen and
Helfat, 2010: 226).
8 NK model is a mathematical model used in Kauffman’s 1993 study.
76 Secondly, IOC-depth provides a sizable advantage in understanding the partner’s norms, habits, and routines (Bell and Pavitt, 1993; Gulati et al., 2009; Belderbos et al., 2011). The ability to identify and explore knowledge on possible potential organizational channels, and to carefully differentiate among them, is strongly enhanced by collaboration experience
(Gulati et al. 2009). Leiponen and Helfat (2010) and Cruz-González et al. (2015) argued that too much of the wider collaboration minimizes the firm’s ability to understand the backgrounds of external organizations (Leiponen and Helfat, 2010; Cruz-González et al.,
2015). In general, it takes a year for firms to develop effective collaboration experience, it mainly come from co-operation with specific partner over the years. It has been demonstrated that the “move along particular trajectories in which the past learning contribution to particular direction of technical change, and in which the experience derived from those paths of change reinforces the existing stocks of knowledge and expertise” (Bell and Pavitt, 1993:
168). Miozzo et al., 2016: 1348) stated that firms are “more knowledgeable about how to manage collaboration partner”, if they have a closer relationship or a better understanding about the partners from previous collaboration. This allows the firm to transfer valuable external knowledge more effectively. Working with the same partners frequently increases the experience of managers and makes co-operation activities more predictable as the information looking for familiar and products and processes development are better understood; and Eisenhardt and Tabrizi (1995) stated that product development problems could be effectively decomposed into solvable sub-problems and increase process flow by eliminating unnecessary steps.
Lastly, the stock of past collaboration and experience provides the base on which firms can develop the capabilities to cope with new technologies. Change is certainly possible, but it is conditioned by the past (Lall 2000). At the same time, previous collaboration with the same
77 partners is more significant than a new partnership for creating superior technological outcomes from future relations with the same partners (Gulati et al., 2009). Learning from existing collaborating organizations is an important aspect of technical development (Nelson and Winter 1982; Lall 2000). Technological knowledge in emerging economies is more localized and firms need to collaborate with local or domestic organizations to access that knowledge. The literature on industrial clusters suggests that IOC with existing local partners
(or experience from domestic organizations) has a significant influence on new technology development (Schmitz and Nadvi, 1999; Guo et al., 2015). Frequent association with same organizations for technological innovation, can lead to significantly deeper understanding of the technical aspect and increased the firm’s absorptive capacity to recognize the key source of information and combine those knowledge for effective TC building. Similarly, Eisenhardt and Tabrizi (1995) claim that learn from experienced co-operation partners important to deal with product development problems more effectively to decomposed into solvable sub- problems and increase the efficiency process flow by eliminated the unnecessary steps, which unable get from new or less experienced co-operation partners. The greater the collaboration experience, the better is the relationship with co-operating partners, making the firm better able to deal with inter-firm differences, as well as mitigating the risks of opportunistic behaviour and TC building in emerging economies. Therefore, the following hypothesis is formulated as:
Hypothesis 2: Inter-organizational collaboration depth has a positive relationship/association with technological capability building.
78 3.3.1.3 IOC and TC: is IOC-depth more important than IOC-breadth?
It has been established that both IOC-breadth and IOC-depth are significantly important for
TC building in emerging economies, but from the evolutionary theory perspective, it is necessary to examine which is the more important. Evolutionary theory perspectives provide more in-deep understanding on emerging economies firms’ motive to form formal external collaboration with different organizational partners for TC building, in particularly IOC- breadth and IOC-depth. This led to the following research question: for TC building, which formal IOC (breadth or depth) is more important for firms in emerging economies?
The main motive for firms in emerging economies engage in IOC-breadth or IOC-depth with various and specific partners to obtain different types of knowledge from external environment. Distant or wider co-operation leads a firm to obtain knowledge breadth from co-operation partners, and intense co-operation specific partners lead to knowledge depth in emerging economies (Guo et al., 2015). Similarly, Petel and Van der Here (2010) used two notions of proximal and distal to refer collaboration depth and breadth respectively; and IOC- depth with few organizations leads to obtain knowledge depth, while IOC-breadth with various partners leads firms to access knowledge breadth.
The motive for firms form IOC-breadth with various or new partners to adds distinctive new variations (different type of knowledge), acquire the knowledge that not exist internally and also reduce risk related to uncertain technological and market environment (e.g. Ferreras-
Méndez et al., 2016; Laursen and Salter, 2014; Cruz-González et al., 2015; Laursen, 2012;
Miozzo et al., 2016). Exploration collaboration characterized by search, variation, risk taking, experimentation, discovery and innovation (March, 1991: 71), which important to a firm to accelerate the development of TC development like R&D activities. For example, Lodh and
79 Battaggion (2014) study using US biotechnology companies show that wider co-operation leads a firm to access technological knowledge breadth and narrow co-operation crucial to obtain technological knowledge depth. Indeed, firms often invest considerable amounts of time, money and other resources in their IOC-breadth for knowledge breadth from external environment. Those efforts to obtain knowledge breadth (new sources and types of information) are crucial for firms’ research and development (R&D) objectives (Laursen and
Salter 2014; Guo et al. 2015; Iorio et al. 2017). Expending firm’s collaboration relationship widely (e.g. new industries), increase the likelihood to access breadth technological knowledge that not available internally, which important to new technology development
(Lodh and Battaggion, 2014). The same time, IOC-breadth also should understand as shallow in nature. Although wider co-operation with different partners leads firms to identify and access new knowledge and resources, however those information are superficial character and only assimilate limited advancements into firm’s internal knowledge pool. This is due to the fact the absorptive capacity of companies to acknowledge the importance of new external knowledge, assimilate it and make use of it as a function in related fields, however, it difficult for the enterprise to understand, and consequently gain from external knowledge in distant channels (Cohen and Levinthal, 1990). As the organization deepens into knowledge and information from external partners, this becomes more easily understandable, allow firms to access valuable distant knowledge, which not able to acquire through a more superficial search (Hsieh and Tidd, 2012; Cruz-González et al., 2015).
Largely, TC literature in emerging economies claims that IOC-depth is more important for firms to enhance innovation and TC building (e.g. see Lall, 1992; 2000: Bell and Pavitt 1993,
1995; Wignaraja, 1998; Guo et al., 2015; Chen et al., 2015). The main motive for firms to form IOC-depth is to increase the external learning experience, access key technological
80 resources and specialized knowledge and also to understand the partner’s norms, habits, and routines that crucial to overcome problems and challenges related to innovation and technological development. Closer association with few numbers of organizations will increase the proximal experience of a firm, which important to obtain knowledge depth (Petel and Van der Here, 2010; Cruz-González et al., 2015). Exploitation collaboration enable firms to enhance productivity and efficiency through refinement, choice, selection, implementation and execution (March, 1991:71), which important at TC building. Lodh and Battaggion
(2014) argued that intense interaction with specific partners is important to enhance the learning experience and trust of co-operating partners, which lead biotechnology firms to access technological knowledge depth and develop new technology. Similarly, Kim (1997) argued that deeper collaboration essential (through the intensity of effort) to enhance the prior knowledge base or past learning (known as absorptive capacity), which critical for firms’ technological learning and TC development (e.g. see Kim 1998 and Cohen and Levinthal,
1990). IOC-depth in emerging economies with universities, government research institutions and technological organizations lead manufacturing firms to access knowledge depth (Katz,
2000; Bell and Figueiredo, 2012; Hansen and Ockwell, 2014). IOC-depth implies stronger ties, which have been shown to be more likely to ease the transfer of complex and tacit knowledge compared to weak ties (Iorio et al., 2017; Dittrich et al., 2007). Likewise, Lall
(1992, 2000) argued that often manufacturing firms in emerging economies’ technological development come from deeper interactions from external organizations like universities and government research institutes (e.g. see Kumar and Siddharthan, 1997; Pietrobelli, 1998).
The knowledge depth only comes from intense co-operation with same partner over time or from previous collaboration experience (e.g. Guo et al., 2015; Chen et al., 2015). Similarly,
Lodh and Battaggion (2014) and Iorio et al. (2017) argued that technological knowledge
81 depth important for new product or process and technology development of a firm. Therefore, based on the balance of the arguments in above, the following hypothesis is proposed:
Hypothesis 3: Inter-organizational collaboration depth has a stronger and more positive association with technological capability building than inter-organizational collaboration breadth.
3.4.2 Hypotheses relating to different organizational partners: IOC-depth and TC building (H4 - H10).
This section discusses the individual types of organizational channel: customers and suppliers
(vertical co-operation), competitors (horizontal co-operation), consultants and private R&D institutes, and universities and government research institutions.
3.4.2.1 Customers and suppliers (vertical collaboration) and TC
The literature suggests that IOC-depth with customers and suppliers (vertical co-operation) allows firms to access information and knowledge about markets, new technologies and innovation (e.g. von Hippel, 1988; Tether, 2002; Nieto and Santamaría, 2007). Co-operating with these partners is easier than with other innovation collaboration partners, although requiring more effort and time to obtain valuable knowledge (Belderbos et al., 2004b;
Laursen and Salter, 2014). IOC with customers (forward linkages) and suppliers (backward linkages) is an important source of knowledge for technology-intensive lines of production
(both process and product innovation) and markets (Bell and Pavitt, 1993; Chandran et al.,
2014), and the shortest route to accessing information about competitors’ movements on technology and innovation (see von Hippel, 1988; Tether, 2002).
Lead users also possess an in-depth understanding of products and technologies that may provide considerable advantage for further development (von Hippel, 1988, 2000; Nieto and
82 Santamaría, 2007), and so are identified as key drivers for technological innovation.
Interaction with consumers often conveys crucial information about customer behavioural activities and preference that are important in fulfilling customers’ current and future needs, which is more relevant to new technology development or upgrading existing products (e.g. computer games, automobiles, etc.) (Kim, 1997, 1998). For example, Samsung transformed their business from semiconductor production to a major manufacturer of consumer electronics following intense collaboration with lead clients who were competitors in the final products (Gnyawali and Park, 2011). Closer IOC with lead clients provides learning opportunities on markets and new customers and technical resources not available internally
(Lamin and Dunlap, 2011; Chen et al., 2015). Active interaction with customers provides critical information to identify the problems or weakness of existing products or processes and at the same time gain access to tacit knowledge for re-building or adding value to existing products or services (von Hippel, 1988; Chandran et al., 2014; Chen et al., 2015).
Tether (2002) and Fritsch and Lukas (2001) claimed that IOC with customers is important for novel product innovations and complex technologies development.
At the same time, suppliers provide greater learning opportunities on new machinery or equipment/component/software development (Pavitt, 1984; Bell and Pavitt, 1993; Colombo et al., 2014; Chen at., 2015). Interaction with lead suppliers allows the firms to learn and understand the new technology that suppliers sell to others (von Hippel, 1988; Zhou and Li,
2010; Guo et al., 2015), and firms can track competitors’ movements on the latest technologies (von Hippel, 1988: Tether, 2002), enabling them to incorporate innovative ideas into production processes and manufacturing activities and also to develop advanced technology. Tether (2002) argued that Japanese automobile and electronics companies tend to co-operate more with suppliers for innovation processes and technological development. In
83 the USA, firms developed new technology for electronic data interchange (EDI) implementation after intense collaboration with their manufacturing suppliers (Angeles et al.,
1998). Further, IOC-depth with suppliers reduces cost-risks (outsourcing activities to suppliers) and lead times of new product development, and also increases market adaptability in other contexts (Chung and Kim, 2003; Nieto and Santamaría, 2007). Suzuki (1993) found that Japanese vertical enterprise groups forming closer relationships with suppliers were associated with greater impact on cost reduction. Similarly, Khan (2013) showed that the
Suzuki car manufacturer’s collaboration with suppliers enhanced their technological capabilities to meet local content requirements and lower the production cost of manufacturing low-cost cars in India.
IOC with customers and suppliers is similarly important for TC in emerging economies (e.g.
Bell and Pavitt, 1993; Hobday, 1995; Kim, 1997; Chen et al., 2015). IOC-depth with customers and suppliers are critically important for firms in emerging economies to access tacit technological knowledge; often firms in developing economies limited opportunities for resources and knowledge (Hobday 1995: 43). Technological capability in emerging economies is generated from complex interactions between firms and suppliers-customers
(known as user-producer technological co-operation) (Bell and Pavitt, 1993, 1995; Lall,
1994). Therefore, we propose the following hypotheses:
Hypothesis 4: Inter-organizational collaboration depth with customers has a positive association with technological capability building.
Hypothesis 5: Inter-organizational collaboration depth with suppliers has a positive association with technological capability building.
84 3.4.2.2 Competitors (horizontal collaboration) and TC
IOC-depth with competitors (horizontal co-operation) is less common but nevertheless an important mechanism to access external knowledge (Hamel et al., 1989; Tether, 2002; Bell and Figueiredo, 2012; Bouncken et al. 2015). Co-operation with rival firms is a risky and complex relationship because of the potential for anti-competitive behaviour (Tether, 2002:
952), and unplanned knowledge spillovers are greater (Miotti and Sachwald, 2003; Laursen and Salter, 2014). Moreover, these risks are much greater in the context of co-operation for innovation with rivals than with other IOC partners like suppliers, lead customers, consultants, private R&D institutes, universities, and government research institutions
(Laursen and Salter, 2014). This is mainly because the content and structural compatibility of competitors are similar, enabling rival firm to benefits from unplanned outward knowledge spillovers9. However, both IOC and TC literature stress the significance of co-operation with competitors as an important mechanism for innovation and capability building. There is an increasingly popular co-operation with rival firms, with over 50% of collaboration between firms within the same industry (Gnyawali and Park, 2011)10. More firms in both developed and emerging economies form IOC-depth with competitors to move up their value chain and gain competitive advantage (e.g see Tether, 2002; Laursen and Salter, 2014; Bell and
Figueiredo, 2012; Chen et al., 2015). There are three reasons why IOC-depth with competitors is important for TC building.
First, IOC-depth with competitors may provide greater opportunities for firms to access external knowledge and resources, because rival companies operating in the same business area have similar needs (Miotti and Sachwald, 2003; Veugelers and Cassiman, 2005).
9 According to Bayona et al. (2003), competitor relationships do not seem to be an important mechanism indeveloping new products. 10 Examples of co-operation with competitor firms: General Motors and Toyota assemble automobiles; Siemens and Philips develop semiconductors; Canon supplies photocopiers to Kodak; France’s Thomson and Japan’s JVC manufacture videocassette recorders (Hamel et al., 1989: 133).
85 Competitor firms may share the competitive advantages (e.g. rivals’ know-how) and extend synergies to achieve win-win results together. Likewise, Nieto and Santamaría (2007) explain that the relationship with competitors as an appropriate mechanism to develop new products innovations with a high degree of novelty may come from rivals’ advanced technologies.
Competitors possess relevant resources and face similar pressures, and a close relationship enables firms to access and develop new technological knowledge (Quintana-García and
Benavides-Velasco, 2004; Gnyawali and Park, 2011). For example, Epsilon (a Malaysian company) modified their pre-existing boiler designs as a result of co-operation with local competitors (Hansen and Ockwell, 2014)11. Similarly, Faems et al. (2005: 243) argued that intense interaction with rivals companies might lead to developing new products (e.g. standard development within emergent technologies or applications) and enhance existing products or services. Bouncken et al. 2015 research results show that collaboration with competitors is advantageous for Germen firms’ incremental innovation (both pre-launch and launch phases), however, not for radical innovation.
Secondly, common motives for forming a relationship with competitors in emerging economies is to deal with risk and cost reductions (Hobday, 2005; Lee, 2012; Hsu et al.,
2009). Hamel et al. (1989) stated that co-operating with local competitors is a low-cost route to gaining technology and market access. IOC-depth with rival firms provides considerable advantages, because competitors might have similar processes or production platforms which would bring down the cost of production and new technology development (Lee at al., 2001;
Hobday, 2005). For instance, in the automobile sector, car manufacturers can jointly develop car parts (e.g. engine, door or window) on the same platform, which brings down the cost and risk of production. Quintana-García and Benavides-Velasco (2004) empirically show that a
11 Epsilon Technology (M) Sdn Bhd is an engineering company in Malaysia.
86 relationship with direct competitors not only allows firms to access new technological knowledge and expertise, but also leads to greater cost reductions. Samsung Electronics and
Sony Corporation, two giant competitor companies, co-operated in project called “S-LCD” to develop flat-screen LCD TV panels (Gnyawali and Park, 2011). Similarly, firms in emerging economies form IOC with competitors to deal with risk and cost associated with large innovation projects (Hsu et al., 2009; Wu, 2014; Lee, 2012). The cost of R&D activities is very high in technology-related industry (manufacturing firms) (Belderbos et al., 2004b;
Aschhoff and Schmidt, 2008). Sharing these costs forces emerging economies firms to collaborate with competitors that have a huge resource base, giving them a sizeable advantage through combining R&D expenses, expertise and other resources (Hansen and
Ockwell, 2014; Chandran et al., 2014).
Thirdly, IOC-depth with competitors in emerging economies not only enhances knowledge and technical exchange; it can also lead to a joint problem-solving relationship (Temel et al.,
2013; Wu, 2014; Wang et al., 2015). Firms tend to collaborate with competitors more when they have common problems outside the competitor’s area of influence, for example regulatory change (Tether, 2002). Competitors have common objectives and goals, and undertaking innovative projects together encourages them to coordinate functions and provide the solutions to problems “on the fly” (Uzzi, 1996: 679). Such joint problem solving makes negotiation and mutual adjustments routine, helping the partners to flexibly resolve problems and improve organizational responses by reducing production errors, and speeding up product and technical competencies development (Teece, 1992). Competitors allow the co-operating firm to gain technical knowledge that enhances technological solutions (Hansen and Ockwell, 2014), work through problems jointly, obtain valuable feedback and increase the possibility of uncovering new solutions or technologies (Uzzi, 1997: 47; Wu, 2014).
87 Competitors are thus an important mechanism for TC building in emerging economies. Wu
(2014) argued that IOC-depth with competitors enhances the TC of Chinese firms in emerging economies. Malaysian biomass power equipment firms have developed the TC, for example the modification of boiler technology, through co-operation with competitors
(Hansen and Ockwell, 2014). Therefore, the following hypothesis is proposed:
Hypothesis 6: Inter-organizational collaboration depth with competitors has a positive association with technological capability building.
3.4.2.3 Consultants & private R&D institutes and TC
In recent years, IOC-depth with consultants and private research institutes or commercial laboratories is increasingly appreciated, rather than relying wholly on internal R&D, not only for saving costs but also as an important source of innovation and capabilities building
(Tether, 2002; Tether and Tajar, 2008), especially in emerging economies (Chandran et al.,
2014; Srinivasan, 2014; Chen et al., 2015). The consultants and private research institutes are also known as specialist knowledge providers (Tether and Tajar, 2008; Sánchez-González,
2014; Srinivasan, 2014).
First, IOC-depth with consultants and private research institutes offers greater advantages in the transfer of specialized and expert knowledge, skills and technologies (e.g. Bessant and
Rush, 1995; Tether, 2002; Du et al., 2014; Love et al., 2014), knowledge developed by themselves or obtained from elsewhere. Tether and Tajar (2008) argued that these specialist knowledge providers offer solutions to a wide variety of problems, including information on business start-up, marketing and manufacturing activities, new technology, and organization strategy developments (Chen et al., 2015; Reichert et al., 2011). By way of example, Bruce and Morris (1998) stated that external product consultants are more effective in providing innovative ideas than are internal designers; even internal staff familiar with the firm’s
88 approach and products fails to deliver innovative solutions. External research institutes and consultants are more capable of producing fresh ideas, and a continuous intense relationship with these partners enables firms to build valuable contextual knowledge of the company and its product technologies. In the context of emerging economies, firms have low levels of internal R&D resources and co-operating with consultants and private research institutes is an effective way to overcome knowledge shortage and also save costs (Chen et al., 2015;
Chandran et al., 2014). IOC-depth with these partners allows firms to access specialist knowledge and find solutions to issues related to innovation process (Chen et al., 2015;
Vrgovic et al., 2012; Hansen and Ockwell, 2014). The result of Srinivasan’s (2014) study shows that the main motive of firms in India to form collaborations with consultants and private research institutes to access technology and operations is related to expertise and cost reduction.
Secondly, an intense relationship with consultants and private research institutes leads to experience sharing (either implicitly or explicitly) as they play the role of “marriage brokers”, linking firms with needs and solutions (Bessant and Rush, 1995; Tether and Tajar, 2008). The partners act like bees in cross-pollinating between companies, carrying experiences and innovation knowledge from one context into another to contribute to solutions to problems
(Tether, 2002; Sánchez-González, 2014; Chen et al., 2015). At the same time, both external research institutes and consultants act as “innomediaries”, helping to formulate technical problems and identify the most suitable problem solvers from their contact with a wide range of specialist services (Bessant and Rush, 1995; Chen et al., 2015). These external research organizations provide access to technical knowledge bases gained through their breadth of experience across contexts and technical projects: high level (technology consulting assignments) and moderate level (process reengineering assignments) in emerging economies
89 (Srinivasan, 2014: 260). Similarly, Chen et al. (2015) claim that consultants’ and private research institutes’ broad reach and expertise may allow the co-operating enterprise to obtain valuable experience and specialized knowledge beyond the enterprise’s own reach and industry field, crucial in avoiding past mistakes. These partnerships focus on concrete and specific issues affecting organizational competitiveness to improve organizational and technological capabilities through learning, particularly benchmarking and transferring the best practices to the firm (Dosi et al., 2000, 2008). Consultants’ relationships are more relevant in manufacturing sectors, as firms often use them to implement new technology (e.g. installation of new production systems) and they transfer their skills and knowledge to the organization (Lall, 1992; Lamin and Dunlap, 2011). For instance, Tether and Tajar (2008) stated that the main reasons for appointing an external consultant like IDEO is to approach a problems from a different perspective and provide new insight to technological innovation. In the Malaysian context, Hansen and Ockwell (2014) showed that “Zeta” used an external engineering consultant to access technical assistance and specialized knowledge for boiler projects.
In sum, IOC-depth with consultants and private research institutes is significant for firms in emerging economies to rapidly catch up or imitate technological innovations developed by their competitors (Bessant and Rush, 1995; Chen et al., 2015). In the light of this, the following hypotheses are proposed:
Hypothesis 7: Inter-organizational collaboration depth with consultants has a positive association with technological capability building.
Hypothesis 8: Inter-organizational collaboration depth with private R&D institutes has a positive association with technological capability building.
90 3.4.2.4 Universities & government research institutions and TC
The major motive for firms forming IOC-depth with universities or other higher education institutes and government or public research institutions is to gain external specialist knowledge, and scientific/technical information to approach new technological frontiers
(Tether, 2002; Miotti and Sachwald, 2003; Belderbos et al., 2004; Rasiah and Chandran,
2009; Temel et al., 2013). Cassiman and Veugelers (2002) have argued that these relationships are important in obtaining new scientific and technical resources much faster, and allowing the firms to accomplish economies of scale from the research projects.
Moreover, intense collaboration with these research institutions leads firms to obtain complementary technological knowledge or assets (including patents and tacit knowledge), which is important for commercial and innovation success in business (Guimón, 2013).
However, governments play an important role in boosting the research activities in universities and public research institutions for competitiveness in industry (Tether, 2002), especially in emerging economies like Malaysia (Rasiah and Chandran, 2009; Chandran et al., 2014) 12. Often firms in emerging economies lack resources and financial capital to undertake larger projects alone, and government intervention in boosting such relationships allows firms to collaborate with universities and public research institutions (see Lall, 1995b;
Chandran et al., 2014). For example, in the late 1980s and early 1990s the Korean government made a huge financial investment in universities and public research institutions, which allowed firms to collaborate with them and develop their TC (Kim 2001). This was a major factor in Korea’s industrial success (e.g. see Hobday, 1994, 1995; Kim, 2000).
12 Malaysian firms have faced increasing international competition, making industrial collaboration with university & government research institutions more importance to cope with global competition and continuous innovation progress (Rasiah and Chandran, 2009; Chandran et al., 2014).
91 Another motive for firms to seek IOC-depth with universities and government research institutes is to reduce risks and the high cost of R&D activities and innovation projects (e.g.
Cassiman and Veugelers, 2002; Guimón, 2013; Chandran et al., 2014). Chen et al. (2015) claim that as firms in emerging economies have a low level of internal R&D competence, universities and public research institutions are a critical source of external knowledge to enhance their understanding of new scientific developments. Similarly, Guimón (2013) argued that public research institutes are more capable of supporting R&D activities of firms in emerging economies than is the private sector (in absent of R&D capacity). In particular, when more public funding is available for firms, interaction with universities or public research institutions is increasingly seen as an inexpensive approach for firms to access scientific and technical knowledge. Furthermore, co-operation with universities and public research institutions is less risky (less knowledge spillover or leakage) than with other types of partner (Belderbos et al., 2004; Chen at al., 2015). Cassiman and Veugelers (2002) claim that universities and government research institutes take a neutral stance and their main aim is to protect co-operating firms’ key information from knowledge spillover or leakages.
IOC-depth with these research institutions also has greater advantages for firms in emerging economies to find solutions to their technical and innovation problems (Chen et al., 2015;
Temel et al., 2013). The direction and pace of technology development depends on universities and government research institutes as major knowledge providers (e.g. new generic technology and product families). Thus, Belderbos et al. (2004) suggested that this relationship is the most effective way to accelerate innovation activities and open new markets and segments. Aschhoff and Schmidt (2006) found IOC with universities and public research institutions it positively influenced new product development of German firms; Lööf and Broström (2008) showed a positive influence on innovative performance of Swedish
92 manufacturing firms. Likewise, Rasiah and Chandran (2009) found research institutions important for firm-level innovative activities (especially technological innovation) in
Malaysia. Lee et al. (2001) produced evidence from 50 interviews with Korean technological start-up companies to show that interactions with universities and public research institutions was important for new technology development and exploring new markets.
In short, IOC-depth with both universities and government research institutions is crucial for firm-level TC building in emerging economies. Chandran et al. (2014) strongly claim that it has a significant influence on emerging economies’ national innovation systems; in particular, creating and reinforcing the network is crucial for innovation and TC development.
Therefore, the following hypotheses are proposed:
Hypothesis 9: Inter-organizational collaboration depth with universities has a positive association with technological capability building.
Hypothesis 10: Inter-organizational collaboration depth with government research institutions has a positive association with technological capability building.
93 CHAPTER 4: RESEARCH METHODOLOGY
4.1 Introduction
This chapter deliberates the research methodology adopted to answer the research questions in Chapter 2. Notably, research methodology is employed in this research to collect the research data (Saunders et al., 2007; Creswell, 2007). Different research strategies and methods can be implemented in a research analysis, but it is critical to link the research objectives and problems to a specific research method to find a better solution. Therefore, a mixed-method approach was employed in this thesis using quantitative and qualitative (which including interviews and three case studies) to answer the research questions and meet overall research objectives.
The chapter begins with the presentation of the research strategy, which explains the aspects of the philosophy of knowledge about ontology and epistemology (Section 4.2). The following selection discussed the research methodology and mixed methods approach for this research and its justification for deployment (Section 4.3). The collections of both relevant quantitative and qualitative data and their effective triangulation were performed in the subsequent section (Section 4.4 and 4.5). The secondary data used for this research is from the Malaysian National Survey of Innovation - sixth series (MNSI-6) manufacturing firm- level data (Section 4.4.1.3). The research variables (dependent, independent and control) and their measurement are also explained here in detail (section 4.4.2). For the qualitative approach, data collected from 30 semi-structured interviews from fifteen firms and two policy-makers (section 4.5). Further, three mini case studies were developed based on three individual firms from fifteen firms (section 4.5.2. and Chapter 7). The final section presents the chapter summary (section 4.6).
94 4.2 Research Strategy
The term “Research strategy” refers to a predetermined plan designed to systematically find the answer to the research questions by testing the study hypotheses and the fit of the conceptual framework to the overall aim of the research. The research strategy also recommends specific data collection methods to support the arguments (Saunders et al.,
2003; Saunders et al., 2009).
The primary objectives of the current research are to seek a deep understanding of how firms build TC through IOC, and to develop a conceptual framework from the literature to guide the research analysis (Maxwell and Loomis, 2003). The rationale for the quantitative method in the mixed-method approach is to test the objective theories by examining the relationship between IOC and TC building in emerging economies, used larger firm-level data. The qualitative method (both interviews and case studies) is used to gain an in-depth understanding from representatives in particular fields (such as CEO, company director, head of the department and senior managers of manufacturing firms) related to the research problem. The findings from both the quantitative and qualitative analysis were delicately triangulated to derive reliable results.
4.2.1 Research Philosophy
Research philosophy is a viewpoint about how data about a phenomenon should be collected, examined, and applied. According to Saunders et al. (2009) research philosophy is essential in deciding the research strategy and methods. Ontology, epistemology, and methodology
(discussed in section 4.3) are three aspects of the philosophy of knowledge, which for this research will highlight on finding an answer to the research questions; and each of them contributes to the design of the study.
95 Ontology concerns the nature of reality that will support and provide a better understanding of the fundamentals and principles of the study (Saunders et al., 2009). It has two approaches: objectivism and subjectivism. Objectivism is dependent on the researcher and social activities, based on one truth. Subjectivism is aligned with an interpretivist approach based on multiple truths dependent on the observer. In this study, the research paradigm of pragmatism has been selected. This paradigm refers to the research ontology has multiple realities. For example, in this research, we will test the hypotheses
(quantitative), and also conduct interviews with selected respondents and incorporated with three case studies (qualitative) to provide multiple perspectives.
Epistemology is the study of knowledge, its nature and limitations related to the research.
Epistemology” is a technical term in research philosophy that identifies the process of gaining knowledge (Maxwell, 2011). According to Schwandt (2007: 87), epistemology is the study of the nature of knowledge and justification. Its concerns knowledge on reality and depends on various forms of belief in reality (Saunders et al., 2009). For instance, “it can be unbiased, generalizable knowledge about the world, or is this knowledge specific to a particular time and place?” (Lee and Lings, 2008: 11-12). There are four major epistemological approaches: logical positivism, constructivism, critical realism, and pragmatism. For the purpose of this study, out of the four options, only pragmatism is chosen, which allows the researcher to use the mixed methods. Furthermore, this approach considers knowledge gained from observable facts and relies more on the consequences of theories. Pragmatism is a research approach that is related to subjective, deductive or inductive and mixed methods. The combination of quantitative and qualitative methods was adopted to achieve the research objectives.
96 4.3 Research Methodology
The central segment of the thesis that pronounces the different approaches followed to ensure the research questions are answered accurately and met the research objectives. We start this section by defining Research and Methodology individually as follows: The term “Research” can be defined as “an activity that involves finding out, in a more or less systematic way, things you did not know” (Walliman and Walliman, 2011, p.7). The term “Methodology” can be referred to as the philosophical framework within which the research is conducted or the foundation upon which the study is based” (Brown, 2006).
Following the above definition, we can summarize that, Methodology can define as how the research is carried out and is driven by ontological and epistemological approaches
(Creswell, 2007; Saunders et al., 2009). This research fuses two specific methods, were the quantitative used secondary data from MNIS-6 and qualitative approach used to collect the primary data (interviews and three case studies), and followed by a triangulation process, which was necessary to combine the results from both methods and validate the results to ensure that it meets the objectives of the research (Creswell and Maitt, 2002; Christou, 2006;
Gill and Johanson, 2010). In the following section, the purpose of employing the simplified mixed-methods approach is discussed in detail.
The following section elaborates the methodical options available to the researcher and specific strategies considered and implemented in this particular research with proper justification while conducting this research. As the methodological framework (Figure 4.0) suggests, the following steps are followed in this research:
1. Quantitative approach: Data collection and analysis of 445 firm-level data from
MNIS-6.
97 2. Qualitative approach: Data collection and analysis of 30 interviews from 15 firms and
two policy-makers.
3. Three case studies were conducted separately (three individual firms were selected
from 15 firms).
4. Triangulation to verify the findings gathered from both quantitative and qualitative
analysis. Incorporated with secondary data such as company annual reports, industry
websites, industry journals, newspapers, business magazines, and industry association
publications.
Quantitative Approach Qualitative Approach
Data Collection for Data Collection for Quantitative-Secondary Qualitative (Primary Data - Data (MNSI-6) 30 Interviews)
Quantitative Data Interview Data 3 Case Studies Analysis Analysis
Case Studies Quantitative Results Interview Results Results
Triangulation- Quantitative, Interview and Case Study result
Interpretation
Figure 4.0: Methodological Framework (Mixed-Method Approach)
98 4.3.1 Mixed-method strategy
Mixed method research is a research technique deployed by the researchers for gathering and analyzing data, that uses both quantitative and qualitative methods to answer the research problems (Tashakkori and Teddlie, 1998; Teddlie and Tashakkori, 2003; Saunders et al.,
2007; Creswell and Plano Clark, 2007). These methods can be used in two separate phases, either in parallel or sequentially (Tashakkori and Teddlie, 2003:11). This study will apply explanatory sequential mixed methods, whereby the quantitative approach is used to analyze the research results and follow by the qualitative approach to explore more detail about research results and provide in-deep understanding on research questions. This is considered as explanatory because the primary quantitative data results will be explained further by the qualitative information. Using the qualitative approach (interviews and case studies) enriches the quantitative results, for example by providing reasons for the results obtained, which a single method might be difficult to explain overall the research problems.
Employing mixed methods in this study will provide an in-depth understanding by triangulating both sets of results and thus, enhancing the validity of the inferences of complex phenomena (Creswell and Plano Clark, 2007). In fact, the concept of triangulation of methods was the intellectual wedge that eventually broke the methodological hegemony of the mono-method purists (Tashakkori and Teddlie, 1998). Mixed methods can offset the disadvantages and methodological limitations of a single approach, with complementary strengths and non-overlapping weaknesses (Johnson and Turner, 2003; Creswell, 2007).
Moreover, this will reduce the methodological limitations of using a single approach. The qualitative methods of this study were conducted to triangulate the quantitative results.
Furthermore, the research topic is focused on emerging economies whereby require an intensive explore (e.g. see Lall 1992; Bell and Pavitt 1993 on technological capability
99 development in emerging economies). The mixed-method approach, this study able to provide in-depth information into the prevalence of emerging economies about inter- organizational collaboration contributions towards technological capability building. This will provide a better quality of research findings and a better explanation of the explored phenomena in Malaysia in the context of emerging economies.
4.3.2 Triangulation
Triangulation helps researchers to confirm, reinforcement or reject the finding of any particular research. It also facilitates validation of data through cross verification, and tests the consistency of the research findings, obtaining through different research methods. There are four kinds of triangulation namely; (1) Data triangulation - involves time, person and space; (2) Researcher Triangulation - it deals with findings of several researchers for an analysis; (3) Theory Triangulation - it deals with multiple theory scheme; and (4)
Methodological triangulation - in which data were collected from the same source, following different research methodology and tools such as questionnaire, one to one interview, case studies, annual reports and observations (Denzin, 1973; Kennedy, 2009).
Since this research deployed mixed-method, therefore, methodological triangulation was appropriate for this thesis. Methodological triangulation is mostly preferred by researchers because of higher credibility that involving multiple methods. This method observes the research phenomenon from a various different perspective, which may be neglected before
(Flick, 2007). In this technique, data are collected through more then one method to investigate if there is a convergence in the findings. This technique contributes to richness and validity of the results, elaborate the findings of another type (Duffy, 1987; Flick, 2007).
In this regard, both qualitative and quantitative methods iteratively to drive a better
100 understanding of the phenomena under any specific research. The significant research community considers that the mixed-method approach is the most appropriate to provide the richness of the outcomes of the study.
It is critical to understand the fact about the idea of performing the triangulation for this research. Especially, to carry out mixed-method research for quantitative and qualitative data- based analysis need appropriate verification, to be accepted internationally. First, the quantitative analysis is based on firm-level data from MNIS-6 used to tested ten hypotheses.
Second, qualitative results obtained from the 30 semi-structured interviews from 15 manufacturing firms and two policy-makers are used to support the quantitative results and expected to provide in-depth insight and further explanation of the relationship between TC building and IOC.
Lastly, three case studies were developed from three individual firms to show how firms are implementing collaborations when dealing with the issue of building technological capability via external collaborations. In order to ensure the validity of this research; the quantitative, interviews and case studies results, along with secondary data - consisted of company annual reports, industry websites, industry journals, newspapers, business magazines, and industry association publications were used to triangulate the overall research findings. This help provides a better understanding and more accurate answers to the research problems, what extent formal collaboration affects TC building, in particular, the relationship between IOC- breadth and IOC-depth with different partners and their impact on TC building.
Triangulation helps this research to deal with issues related to bias, such as measurement bias, sampling bias, and procedural bias (Denzin 1973; Tashakkori and Teddlie, 1998).
101 4.4 Quantitative Approach
4.4.1 Data Collection
The data for this research is drawn from the Malaysian National Survey of Innovation (MNSI
-6). The MNSI adopted from a long tradition of research on innovation surveys by the
Organization for Economic Co-operation and Development’s (OECD) Oslo Manual (1992,
1997, 2005). The subsequent segments provide a detail description of the nature of the innovation survey and highlight the strengths and weaknesses of the innovation survey. The next section illustrates the Malaysian National Survey of Innovation (MNSI). The final section discusses the sample of this study.
4.4.1.1 Nature of innovation surveys
The first innovation survey was carried out at Britain in the 1950s by the Science and
Industry Committee of the British Association (Archibugi and Pianta, 1996; Mairesse and
Mohnen, 2010). Since then, these surveys have attracted attention in several other countries.
In the United States, the first innovation survey was conducted in the 1960s by the National
Science Foundation, and in Germany in the 1980s. OECD and Eurostat 13 made a joint venture to formalize and standardize innovation surveys in the Oslo Manual, and published the first report in 1992 (Mytelka et al., 2004; Smith, 2005). Subsequent versions were published in 1996 and 2005. Following Archibugi and Pianta’s (1996) framework, innovation surveys are categorized by either their object approach or subject approach. The object approach focuses on the data collection for the survey at individual level (individual innovation), and the subject approach on the firm-level as a whole, irrespective of whether the individual company is engaged in innovation activities or not.
13 Eurostat is the statistical office of Europe (28 countries of the European Union as at the time of writing). For more information visit http://ec.europa.eu/eurostat/about/overview
102 OECD Oslo Manual (1997, 2005) innovation survey questionnaires have been widely used
(e.g. see Young, 1996; Leiponen and Helfat, 2010). Many countries worldwide conduct innovation surveys, but several, including Malaysia, use only the subject approach (MNIS,
1994, 2012). In Europe, the Community Innovation Survey (CIS) is based on firm-level data from all member states (Eurostat, 2014). The New Partnership for African Development
(NEPAD) is the pioneer for the launch of innovation surveys in 20 African countries
(Mairesse and Mohnen, 2010). The number of countries conducting innovation surveys increases every year, especially in developing countries (see Bell and Figueiredo, 2012: 36), because of their value in assessing the country’s development in relation to innovation and understanding the nature of their innovation activities in comparison to neighboring countries.
The innovation survey questionnaires are designed specifically to investigate the innovative phenomenon at the firm-level and to capture the systemic nature of innovation activities between firms. They are characterized as subject-oriented because a firm’s representatives are asked directly to describe their innovative activities (Smith, 2005; Laursen and Salter,
2006, 2014). The information provided includes demographic features (e.g. the respondent’s details, company profile), general information about the firm (main business activities, turnover, number of employees year of establishment, sector and industry type), information about innovations (product, process, marketing and organizational innovation), innovation activities and expenditure, objectives and effects of innovation, government support for innovation, innovation collaboration, sources of information for innovation, patent and other protection methods, and factors hampering innovation14.
14 Innovation information based on Malaysian National Survey of Innovation (MNSI) and other innovation surveys (like CIS survey).
103 Innovation survey data have been extensively used by researchers worldwide to understand the patterns of innovation (e.g. see Tether, 2002; Miotti and Sachwald, 2003; Cassiman and
Veugelers, 2002; Faems et al., 2005; Arranz and Arroyabe, 2008; Chandran et al., 2014;
Miozzo et al., 2016), especially in developed countries (extensively by European countries) and more recently in emerging economies like Malaysia, Indonesia, Brazil, Argentina and
China. Using innovation survey data for any thesis or research publications has advantages, but also drawbacks. The following section highlights the strengths and weaknesses of innovation survey data.
4.4.1.2 Strengths and weaknesses of innovation survey data The major strengths of innovation survey data sets are as follow: 1. The innovation survey data sets are available over a long period, for decades in some instances in Europe and other parts of the world (Archibugi and Pianta, 1996; Cassiman and Veugelers, 2002). This survey is not only conducted in developed countries but is also carried out in developing and emerging economies (e.g. Thailand, Vietnam, Nigeria, Tanzania)15. In Malaysia, the innovation survey data has been accessible since 1994 (MNIS, 1994; Lee, 2008) 16. 2. The survey provides information related to technological and non-technological innovation and also relevant to policymakers is the major factors for which many countries are imposed the innovation surveys. The long-serving innovation survey data play a crucial role in designing several policies, particularly in innovation policy (Mairesse and Mohnen, 2008). 3. Innovation surveys are highly standardized and are reliable sources of information (Mairesse and Mohnen, 2008; De Faria and Dolfsma, 2011). The survey is usually conducted by government officials and statistics agencies. For example, Community Innovation Surveys (CIS) in the UK are undertaken by the Office for National Statistics (Tether, 2002), and in Malaysia by the Malaysian Science and Technology Information Centre (MASTIC).
15 The Bogota Manual (how to conduct innovation survey in emerging economies) is used to include questions related to developing countries in OECD 2005 (Jaramillo et al., 2001). 16 The first Malaysian innovation survey was conducted in 1994 by the Ministry of Science, Technology and the Environment Malaysia (MSTEM), and Malaysian Science and Technology Information Centre (MASTIC).
104 4. The usage of innovation survey data among researchers is consistently increasing in their studies and research publications (OECD 2005, OECD 2013; OECD 2013b; Mairesse and Mohnen 2008). As the data is harmonized across the countries, it is internationally comparable and enables researchers to conduct comparative analysis such as cross- country, panel data, and longitudinal studies. 5. Innovation survey data can be linked to secondary data like patent data, R&D data, and financial or accounting data. 6. The data covers a broad range of information of firms that are engaged in innovation activities, and technological and non-technological aspects (Belderbos et al., 2004, 2004b; Leiponen and Helfat, 2010). This enables researchers to analyze several key research issues related to innovation and also help to formulate a comprehensive picture of the innovation activities of a country.
The paragraphs that follow discuss the limitations of the innovation survey: 1. Innovation surveys are frequently criticized for having qualitative data, such as dichotomous or binary variables (e.g. yes/no questions), categorical data (scoring the importance of innovation), and unordered categorical or nominal variables (e.g. objectives and effects of innovation) (Young, 1996; Smith, 2004)17. 2. Most innovation surveys are subjective in nature because they are derived from the personal judgment of the participants (Archibugi and Pianta, 1996; Smith, 2004). For example, the questions requiring answers such as “only new to your firm”, “new to your market” or “new to the world” are very subjective and require a good knowledge of the market for a person to be able to answer them. 3. Several variables or questions in the innovation survey demand confidential answers and maybe censored or have selectivity issues (Smith, 2004; Mairesse and Mohnen, 2008; Crespi and Maffioli, 2014). Surveys focus on innovative firms, with very few questions relevant to non-innovative firms18. 4. The majority of surveys do not take into account firms with fewer than ten employees (Smith, 2004; van Beers et al., 2015)19, and van Beers et al. (2015: 13) claims that micro-
17 To minimize the measurement errors, appropriate econometric techniques such as logistic regression, multinomial, Tobit model and others are required (Mairesse & Mohnen, 2010). 18 Innovation surveys do include questions for non-innovative firms related to factors that are hampering innovation activities. In relation to this thesis, MNSI include questions on factors that are hampering innovation activities and additional questions on awareness of National Innovation Model (NIM) for non-innovative firms in Malaysia. 19 Researchers are unable to do any studies or analysis related to micro-innovative firms due to unavailable innovation survey data. In the Malaysian innovation survey, the sample covers firms with five or more employees and the sample data are better than in many other countries (e.g. CIS data of European countries).
105 firms are a forgotten group of innovators; their data are also important in understanding the nature of innovation activities in both developed and developing countries. 5. Innovation survey data is heavily criticized for the period of the sample and its cross- sectional nature because the indicators are either similar or overlapping (Mairesse and Mohnen, 2010: 1138; Leiponen and Helfat, 2010). 6. The time span of many innovation surveys is too short for firms to realize the effects of some of their innovations (Mairesse and Mohnen, 2008; Crespi and Maffioli, 2014), with data collected based on a 3-year interval20.
Using innovation survey data provides both strengths and weaknesses in any research. In this study, we take all possible steps to deal with the weaknesses, including econometric issues like simultaneity bias and endogeneity problems in data analysis (see Section 4.5). The following section describes the Malaysian National Survey of Innovation (MNSI).
4.4.1.3 Description of Malaysian National Survey of Innovation (MNSI)
The first Malaysian National Survey of Innovation (MNSI) was carried out in 1994 to collect information on product and process developments. It was followed by MNSI-2 (1997-1999),
MNSI-3 (2000-2001) and MNSI-4 (2002-2004), which all focused on collecting information only on manufacturing firms. Subsequently, the scope of the survey was expanded to include service industries: MNSI-5 (2005-2008) and MNSI-6 (2009-2011)21. The MNSI surveys and the results are crucial for the Malaysian Government to formulate and modify the strategies and operations to enhance innovation, technological development, and commercialized practices (MNSI, 2012).
20 In Europe, CIS data is collected every four years with a 3-year time span Fo.r example in the UK: CIS 1 (1990–1992), CIS 2 (1994–1996), CIS 3 (1998–2000), and CIS 4 (2002–2004). Since 2007, the surveys have taken place every two years with a 3-year time span: CIS 5 (2004-2006), CIS 6 (2006-2008), CIS 7 (2008-2010), CIS 8 (2010-2012), and CIS 9 (2012-2014). The one year overlap between the CIS waves has led to overestimation of the persistence of innovation and requires appropriate econometric techniques to minimize the errors (e.g. see Smith, 2004; Mairesse and Mohnen, 2008, 2010). 21 MNSI-5 and 6 focused on innovation among small, medium and large firms in both manufacturing and service sectors.
106 The primary reason for the use of MNSI data in this research, the survey conducted with the purpose to measure the level of activity and the status of innovation, particularly on technological development such as R&D activities, implementation of new or improved characteristics of the product, process, methods in a business firm, workplace organization and the important of external collaboration for innovation. This extensive information allowed to captured both key variables in this thesis; the dependent variables - (e.g. Tsai,
2004; Iammarino et al., 2008; Iammarino et al., 2012; Reichert and Zawislak, 2014) and independent variables - breadth and depth collaboration (e.g. Laursen and Salter 2006, 2014;
Tether and Tajar, 2008; Leiponen and Helfat, 2010; Love et al., 2014) (for more details refer
Section 4.4.2).
The data for this research are drawn from the sixth series of (MNSI-6, which was commissioned directly by two Malaysian Government officials: (1) Malaysian Science and
Technology Information Centre (MASTIC), and (2) Ministry of Science, and Technology and
Innovation, Malaysia (MOSTI). The survey questionnaire and methodology were adopted from the third edition of the Oslo Manual (OECD, 2005) and harmonized with the CIS-4 questionnaire. The main objective was to provide information on the innovation activities of
Malaysian firms with five or more employees, during 2009 to 2011 22. The survey was completed by senior managers responsible for business operations, financial managers, or heads of R&D or corporate planning. The survey data was collected by post, fax, email, phone interview, seminar and online interview.
22 Other objectives of MNSI-6 are: to measure the knowledge and awareness of private sector companies on the National Innovation Model developed in 2007; to identify the major obstacles in implementing innovation; to gain information on the profile of innovative companies; to identify and develop parameters on innovation that enable benchmarking and comparison with other countries in terms of innovation and competitiveness; and to provide suggestions for the purposes of promoting and enhancing the country’s level of innovation in the manufacturing and service sectors (source: MNIS-6 report 2014).
107 A total of 5,293 questionnaires were distributed to manufacturing and service firms recorded in the Department of Statistics Malaysia (DOSM), and 2,006 were returned (38%).23 From the sample collected, around 1,682 firms (84%) were usable, with 737 firms from the manufacturing sector and 945 are from services. The final representative sample in the manufacturing sector consisted of 445 innovative and 292 non-innovative firms, as shown in in Table 4.1 and Figure 4.1.
Table 4.1: Innovative and non-innovative manufacturing firms. Manufacturing
Firms Percentages (%) Innovative 445 60 Non-Innovative 292 40 Total 737 100
40% (292 Firms) 60% (445 Firms)
Innovative Non-Innovative
Figure 4.1: Innovative and non-innovative manufacturing firms.
23 The survey covered all 13 states (Kelantan, Perak, Perlis, Melaka, Kedah, Negeri Sembilan, Terengganu, Pulau Pinang, Pahang, Sabah, Johor, Sarawak and Selangor), and the three federal territories (Kuala Lumpur, Putrajaya and Labuan) of Malaysia. It covered all the firms in both manufacturing and services sectors that are classified under the Malaysian Standard Industrial Classification 2008 (source: MNIS-6 report 2014).
108 4.4.2 Variables and Measures
This section discusses core variables of this research such as dependent variables - TC input and TC output, independent variables - IOC-breadth and IOC-depth, and specific measures such as control variables - industry type (sub-sectors), firm size, year of establishment, and type of ownership in the following sections.
4.4.2.1 Dependent variables
The dependent variables, drawn from MNSI, are two TC measures: input and output. In previous studies, researchers have used various measures of TC, such as R&D investment,
R&D intensity, R&D resource allocation, R&D efforts, patents, and new or significant improved products or services and processes. The major weakness of those studies is that they measure TC from either the input or the output perspective, although a few used both indicators (e.g. Tsai, 2004; Iammarino et al., 2012; Reichert and Zawislak, 2014). TC input refers to R&D activities or investments 24 that firms do in order to develop TC, and TC output refer to outcomes such as patents, new products or services and new processes.
Evidence suggests that TC can be signalled from both input and output perspectives simultaneously (Lall, 1992; Kim, 1999; Wignaraja, 2002; Tsai, 2004; Iammarino et al., 2012;
Reichert and Zawislak, 2014).
The main reason for using TC input as a measurement is to take into account the efforts that the firms are taking (or investing) made to develop capabilities, as not all the firms can complete the development of new products (or services) or processes during the period of the survey. The MNSI questionnaires were based on a three-year period of innovation activities, and some firms, which had invested in R&D activities, did not show results within that
24 In innovation survey questionnaires (UK-CIS, MNSI and other OCED surveys), R&D activities are used as proxy for innovation activities.
109 period. Some R&D investment in the long term becomes a capacity to the firms to develop products or processes, which will not have been captured in the survey. Therefore, firms’ efforts (such as investment or undertaking R&D activities) to develop capabilities are considered an important aspect of TC measurement among firms in emerging economies.
TC outputs of new products (or services), or efficient processes are better indicator than patents, an output measurement others have used (e.g. Lall, 1992; Wignaraja, 2002;
Iammarino et al., 2008; Reichert et al., 2011; Iammarino et al., 2012). Much of the traditional literature used patents as important indicators of TC: number of patents, patents obtained or submitted or registered, and patent impact (e.g. Archibugi and Pianta, 1996; Lee et al., 2001;
Tsai, 2004; Coombs and Bierly, 2006; Figueiredo, 2009; Reichert and Zawislak, 2014; Lamin and Dunlap, 2011). However, in emerging economies, new products (or services), or processes measurement are more effective ways of capturing TC. Coombs and Bierly (2006) argued that some firms, especially in emerging economies, may not have formal R&D, but could develop new products or processes as part of their normal manufacturing operations, and without registering a patent, again making output a more reliable indicator. There are several other limitations of patents as indicators of TC (Archibugi and Pianta, 1996: 453-454;
Coombs and Bierly, 2006: 425). First, not all innovations or inventions are patentable; for example, all software is legally protected by copyright. Secondly, not all innovations are patented; companies may protect them by industrial secrecy. Thirdly, companies have different attitudes to patenting in their domestic market and in foreign countries, depending on their expectations for exploiting their inventions commercially. Every country has its own patent office, with different procedures and application processes, which may be more difficult for foreign companies.
110 Therefore, TC input (R&D activities) and output - new products (or services) and processes are used as indicators of TC for this research analysis. Capturing TC from both input and output indicators are important for Malaysian firms, since the nature of emerging economies firms building capabilities is a step-by-step process and sometimes it takes years to successfully develop TC. The next sections discuss the selection process for TC input and output in more detail.
4.4.2.1.1 TC Input
TC input (R&D activities) is identified in MNSI question 7.2, which asks whether the firm, during the three-year period 2009-2011, engaged in R&D activities (there are nine sub- questions, 7.2a-7.2i), coded as 1 for “Yes” and 0 for “No”.25 If firms have engaged in any one of these nine R&D activities, they are considered as having TC input.
4.4.2.1.2 TC Output
TC is identified by two output-oriented activities, the first identified from questions 2.1(a) and (b), which ask whether the firm has introduced new or significantly improved products
(or services).26 The second output measurement is based on question 3.1 on process, which asks whether the firm has implemented: 3.1 (a) new or significantly improved methods of
25 Question 7.2: (a) In-house R&D (intramural R&D): Creative work undertaken within your company on an occasional or regular basis to increase the stock of knowledge and its use to devise new and improved goods, services and processes; (b) Acquisition of R&D (extramural R&D): Same activities as above but purchased by your company and performed by other companies (including other companies within your group) or by public or private research organizations; (c) Acquisition of machinery, equipment and software: Purchase of advanced machinery, equipment and computer hardware or software to produce new or significantly improved goods, services, production processes, or delivery methods; (d) Acquisition of external knowledge: Purchase or licensing of patents and non-patented inventions, know-how and other types of knowledge from other companies or organizations; (e) Training: Internal or external training for your personnel directly aimed at the development and/or introduction of innovations; (f) Market introduction of innovations: Activities for the market preparation and introduction of new or significantly improved goods and services, including market research, and launch advertising; (g) All forms of design: Expenditures on design functions for the development or implementation of new or improved goods, services and processes. Expenditure on design in the R&D phase of product development should be excluded; 7(h) Preparation for marketing innovation: Activities related to the development and implementation of new marketing method including acquisition of other external knowledge and other capital goods that is specifically related to marketing activities; and (i) Preparation for organizational innovation: Activities related to the development and implementation of new marketing method including acquisition of other external knowledge and other capital goods that is specifically related to marketing activities. 26 2.1(a) New or significantly improved products? and 2.1(b) New or significantly improved services?
111 manufacturing; 3.1(b) logistics, delivery or distribution methods; and 3.1(c) supporting activities for their processes.27 Since, both product (or services) and process are used to measure TC, if firms in Malaysia have introduced new or significant improved products or services or process (at least one out of five innovation outcome), then it will be considered that firms have developed technological capability, it will then be coded with the value 1 if firms stated ‘Yes’ and 0 if they state ‘No’.
4.4.2.2 Independent variables
The variable IOC for innovation is widely used in innovation studies and open innovation literatures as either an independent or dependent variable (e.g. Tether, 2002; Laursen and
Salter, 2006, 2014; Iammarino et al., 2012, Miozzo et al., 2016). In recent years, more studies have focused on developing and emerging economies using similar variables from innovation survey questionnaires (e.g. Lee, 2008, 2012; Crespi and Zuniga, 2012, Chandran, et al. 2014).
Following Laursen and Salter (2006), IOC-breadth and IOC-depth have been used to measure the degree of co-operation or firm-level openness in several innovation studies (e.g. Katila and Ahuja, 2002; Tether and Tajar, 2008; Leiponen and Helfat, 2010; Laursen and Salter,
2014; Love et al., 2014).
The independent variables in this analysis were drawn from MNSI question 12.1, directed to
Malaysian firm managers or higher officials to find out whether their company has collaborated in each of the following organizational sources for innovation: (1) suppliers, (2) clients or customers, (3) competitors, (4) consultants, (5) private R&D institutes and commercial laboratories, (6) universities, and (7) government or public research institutes.
27 Question 3.1: (a) New or significantly improved methods of manufacturing or producing goods or services? (b) New or significantly improved logistics, delivery or distribution methods for your inputs, goods or services? (c) New or significantly improved supporting activities for your processes, such as maintenance systems or operations for purchasing, accounting, or computing?
112 Table 4.2 lists all these seven organizational sources. Two variables (breadth and depth) are
used to measure the degree of IOC for innovation: IOC-breadth is the number of
organizational channels or external sources of knowledge that firms rely upon for their
innovative activities (Laursen and Salter 2006, 2014). The second variable, IOC-depth is the
extent to which firms draw deeply from the different organizational sources for innovative
activities (Katila and Ahuja 2002; Laursen and Salter 2006).
Table 4.2: Collaboration with Organizational Partners (importance of partners) for innovation activities of Malaysian firms (n=445). Percentages Organizational Partners (%) Not Relevant Low Medium High 1 Suppliers of equipment, materials, components, 57 3 10 30 services or software 2 Clients or customers 58 1 9 32 3 Competitors and other companies in your 63 11 9 18 industry 4 Consultants 64 7 13 17 5 Commercial laboratories and private R&D 63 11 12 15 institutes 6 Universities or other higher education 62 11 13 15 institutes 7 Government or public research institutes 59 10 14 18
In early analysis Laursen and Salter (2006) used both breadth and depth to reflect the degree
of co-operation relevant to the openness of the firms in searching external knowledge for
innovation activities. For breadth, they used all three levels of co-operation, namely ‘high’,
‘medium’, and ‘low’, and to define depth they used only ‘high’ co-operation. However, in the
innovation literature there is some inconsistency about measurement of breadth co-operation
or external search the breadth variable. For example, Leiponen and Helfat (2010) and
Laursen and Salter (2014) defined collaboration breadth slightly different from Laursen and
Salter (2006), which they used of only ‘high’ and ‘medium’ levels of collaboration (refer
Table 4.3 below for more information). In other words, they did not consider firms to be
engaged in co-operation if they stated ‘low’ level of collaboration.
113 In order to understand the nature of emerging economies and firm’s characteristic of collaboration activities, it is important to include all three levels of co-operations (low, medium and high). Taking account of ‘low’ level of collaboration in emerging economies is important for this research, since several studies on emerging economies have highlighted firms having weak collaboration with external organizations (e.g. Lall, 1992; Bell and Pavitt,
1993, 1995; Wignaraja, 1998). Therefore, we use all three degrees of collaboration ‘low’,
‘medium’ and ‘high’ to measure the IOC-breadth and only ‘high’ for IOC-depth. The following section will discusses in more detail the selection process of independent variables.
Table 4.3: Define the Independent variables based on Innovation Survey (e.g. UK CIS) Laursen and Salter (2006) External search breadth External search depth Using ‘low’, ‘medium’ and ‘high’ Using ‘high’ only. Leiponen and Helfat (2010) and Laursen and Salter 2014 External search breadth Using ‘medium’ and ‘high’ only. How independent and dependent variables are defined in this study: Inter-organizational collaboration breadth Inter-organizational collaboration depth Using ‘low’, ‘medium’ and ‘high’ Using ‘high’ only. Source: Author’s own elaboration.
4.4.2.2.1 IOC-breadth
To measure IOC, firms were asked to indicate whether or not they had collaborated with or used any source of knowledge for innovation activities with any of the seven types of organizations listed above, in the period 2009-2011.
To measure IOC-breadth consistently with previous studies (e.g. Laursen and Salter, 2006,
2014; Love et al., 2014), we coded this as a binary variable to indicate the importance of collaboration, with value of 1 when firms indicate either (“low” or “medium” or “high”) collaboration with organizational sources and coded 0 when firms indicate (“not relevant”) that they did not collaborate with any of the organizations or no sources of knowledge are used. The results for the seven organizational sources are summed with the firm scoring 0, when firms are not involved in collaboration with any of the seven external organizations,
114 and 7 if firms collaborate with all the organizations. IOC-breadth appears to have a high degree of internal consistency (Cronbach’s Alpha Coefficient = 0.97). In order to apply logistic regression, we normalize the variable by dividing it by the total number of organizational sources (7), so that the resulting variable takes a minimum value of 0 and a maximum of 1.
4.4.2.2.2 IOC-depth
Following Laursen and Salter (2006), we used only “high” co-operation to construct IOC- depth variable. We coded 1 when firms indicated a (“high”) level of co-operation and 0 when firms indicate “not relevant” or “low” or “medium” otherwise. Similarly, having summed the results, the total could be 0 to 7. Cronbach’s Alpha Coefficient was 0.83. Then total score will be normalized by dividing it by seven to make it minimum value of 0 and maximum of
1.
4.4.2.3. Control Variables
A number of control variables suggested as relevant by the literature and prior studies. The core investigation of this study, examine the affects of formal inter-organizational collaboration on technological capability building, other determinants to this relationship must be considered for deepening the understanding in the context of emerging economies.
To control for other potential illustrations of the research hypotheses, we incorporate control variables in the main model of logistic regression, for industry type, firm size, year of establishment and type of ownership (e.g. see Iammarino et al., 2012; Lamin and Dunlap,
2011), as follows.
115 4.4.2.3.1 Industry type (sub-sectors)
The first control variable is industry type (SUB-SECTORS). The two main types, high- technology and low-medium technology (LMT) are extensively used in the literature (see
Bayona et al., 2001; Arranz et al., 2008; De Faria et al., 2010; Sánchez-González, 2014;
Alvarez and Iske, 2015), and we use both as the control variable for sub-sectors. These two sub-sectors are incorporated in the research model to observe changes to the research hypotheses (positively or negatively) that relate to high-technology and LMT firms. It is crucial to identify which industry type can build stronger TC through IOC. However, there is a lack of empirical evidence in the context of emerging economies (see Lamin and Dunlap,
2011; Molina-Domene and Pietrobelli, 2012). The innovation literature is suggested that high-technology and LMT firms have significant influence on TC development in emerging economies. For example, Sánchez-González (2014) and Alvarez and Iske (2015) show that high-technology firms tend to have stronger effects on innovation performance and activities than LMT firms. See Table 4.4 and Figure 4.2.
Table 4.4: The main industry type of the analysis. Industry Type Firms Percentages (%) High-Technology 219 49 Low-Medium Technology (LMT) 226 51 Total 445 100
Industry Type (Sub-sectors)
49% 51%
High-Technology
Low-Medium Technology (LMT)
Figure 4.2: The main industry type of analysis.
116 4.4.2.3.2 Firm Size
The second control variable is the firm size (SIZE). Traditionally, in the innovation literature firm size is widely used as a main control variable in firm-level analysis, as a determinant of
R&D co-operation, innovation collaboration and innovation performance (e.g. see Cassiman and Veugelers, 2002; Belderbos et al., 2004; Laursen and Salter, 2006). This study similarly incorporates firms’ size (large firms and SMEs) as a control variable. It is measured by a single item representing the number of employees, SMEs having 5 to 100 employees and large firms more than 100 (MNIS, 2012; OECD, 2005). Firm size had different effects on firms' performance and innovation outcomes (Cassiman and Veugelers, 2002; Belderbos et al., 2004b). In relevant to this study, firm size turns out to be another crucial determinant of
TC development and IOC, especially in emerging economies (see Lall, 1992, 1994;
Wignaraja, 1998). Large firms tend to have stronger absorptive capacity in building capabilities than do SME firms. However, limited empirical evidences are available in existing studies in related to firm size in context of emerging economies (see Molina-Domene and Pietrobelli, 2012). See Table 4.5 and Figure 4.3.
Table 4.5: Innovative firms based on firm size. Firms Size Firms Percentages (%) Large 187 42 SME 258 58 Total 445 100
Firm Size
42% 58%
Large
SME
Figure 4.3: Total innovative firms based on firm size.
117 4.4.2.3.3 Year of establishment
The firm’s year of establishment or the firm’s age (YEAR) is the third control variable. Age has a greater influence on TC building, especially among emerging economies (e.g. see
Pietrobelli, 1998; Lamin and Dunlap, 2011; Lee, 2012). For example, new technology-based companies have little effect on the innovation process and technological status, compared to well-established companies (e.g. Iammarino et al., 2012). In this research, a dummy variable is created for the firms that are established before the year 2000 as well established firms and after year 2000 as new firms. This will allow us to identify which firms (established or new firms) have a greater influence on TC development. See Table 4.6 and Figure 4.4.
Table 4.6: Innovative firms based on firm year of establishment. Year of Establishment Firms Percentages (%) New Firm 197 44 Established Firm 248 56 Total 445 100
Year of Establishment
44% 56%
New Firm
Established Firm
Figure 4.4: Total innovative firms based on year of establishment.
118 4.4.2.3.4 Type of ownership
The final control variable is the type of ownership (OWNERSHIP). Prior studies suggested that firm ownerships an important control variable in the TC literature, especially in emerging economies (e.g. see Lall, 1992; Bell and Pavitt, 1995; Kim, 2000). Consistent with the literature, this study used local firm (headquarters located in Malaysia) and foreign firm
(headquarters located outside of Malaysia) as two types of firm ownerships. This variable stands for local firm, which the headquarters located in Malaysia and it is a dummy equal to
1, and for foreign firm coded as 0. In emerging economies, foreign MNCs tend to have larger capital, wider networks, and more dynamic and global markets than local firms, especially in term of TC development (e.g. see Bell and Pavitt, 1993; Pietrobelli, 1998; Rasiah and
Chandran, 2009; Lee, 2012). For example, the study of Chandran et al. (2014) stated that foreign-owned electronics firms in Malaysia have better absorptive capacity than domestic firms in relation to innovation and technological development. See Table 4.7 and Figure 4.5.
Table 4.7: The main type of ownership of the analysis. Type of Ownership Firms Percentages (%) Local Firm 380 85 Foreign Firm 65 15 Total 445 100
Type of Ownership
15%
Local Firm 85% Foreign Firm
Figure 4.5: Total innovative firms based on the type of ownership.
119 4.5 Qualitative Approach
4.5.1 Background
The qualitative approach is a strategy, which draws consideration to the research data collection and explanation by words rather than numbers (Tashakkori and Teddlie, 1998;
Saunders et al., 2007). It gives the researcher a detailed understanding of the rationale or theory derived from the quantitative data and thus increases its effectiveness of the quantitative research (Creswell and Plano Clark, 2007). The primary purpose of the qualitative approach employed in this study is to ensure the follow-up of the results obtained from the quantitative method - examining the influence of collaboration on TC building.
Apart from that, this approach also used to address the research questions with selected respondents. Qualitative analysis of both interviews and case studies findings in rich data that gives an in-depth picture of how manufacturing firms incorporate collaboration as main strategies to develop their TC. In particular, who do they collaborate, how they collaborate, and what mechanism they used to collaborate in building TC.
According to Creswell (1998), the main advantage of the qualitative approach is providing an in-depth understanding by application of the methodological practice and able to explore all circumstances in a natural setting. It also gives the researcher a broad range of choices
(Saunders et al., 2009; Teddlie and Tashakkori, 2003), allowing a researcher to ask supplementary questions, find detailed answers, and overcome the weaknesses in using a single method, and fostering relevant theories. In summary, the qualitative approach
(interviews and three case studies) provides a deeper contextual understanding of the effects of IOC on TC building in emerging economies.
120 4.5.2 Data collection and interview strategy
In innovation studies in particular to technological capability development literature, there are various approaches for qualitative data collection. Creswell (2003) has stated that the four main strategies of qualitative data collection are observation, interviews, and the use of audio-visual materials and official documents. The most popular approach is the interview
(Tashakkori and Teddlie, 1998; Teddlie and Tashakkori, 2003). Saunders et al. (2007) distinguished three types of interviews, namely; structured, semi-structured, and unstructured.
The first involves a set of standardized questions; it’s more to closed-ended questions, while semi-structured interviews use open-ended questions. The unstructured interviews are informal; without a well-prepared list of questions, they explore a general fact or situation in detail.
The data collection method used in this research gathers the primary data through semi- structured interviews. This method obtains insight and in-depth data about the firm’s technological capability building and inter-organizational collaboration about Malaysian manufacturing firms. The communication channels used here are face-to-face and telephone/Skype based interviews to increase the participation level and engagement. More details about the semi-structured interview procedure follow.
4.5.2.1 Data Collection
According to Malhotra and Briks (2003), there are two categories of qualitative approaches; direct and indirect. This study uses the direct approach to gather primary data from 30 interviews from 15 manufacturing firms and two policy-makers from Malaysian. Further, three case studies were developed based on three individual companies (details of the case were explained in section 4.4.2.2 and Chapter 7).
121
A total of 30 interviews conducted from 15 manufacturing firms and two policy-makers. The full details of the interview data shown in Table 4.8 below. Twenty-eight interviews were conducted from 15 firms to collect information to answer the research questions. The two meetings from policy-makers used to understand underpinning policy issues related to TC building in Malaysia. Policy-makers views consider crucial for this research finding and also able to map important policy implications towards policy-makers and practitioners.
The interviews took place in two periods of time — the first phase of interviews with 22 respondents in Jun - September 2016. The second phase of interviews with eight respondents undertaken in February 2019. This data was collected to develop three case studies based on three individual companies. From total interview respondents, 23 face-to-face interviews, and the other seven are phone/Skype interviews. The meeting with each respondent lasted between 30 to 90 minutes, and the full conversations recorded. From 15 firms, nine of them were local or domestic firms, and the remaining six were international firms (the headquarters located outside of Malaysia). The business sectors of respondent companies, eight low-medium technology firms, and seven high technology firms. In terms of size, eight are large firms, and the remaining seven firms are SMEs. The following section will discuss the methodological procedures applied in this research created by Malhotra and Briks (2003).
122 Table 4.8: List of Interviews of Manufacturing Firms. NO Company Code Year Established Local/ Sector Size High-Tech Interviewees Date/mode of interview Length of International /LMT Interview 1 FBLLMT-1 1971 Local Food & Beverage Large LMT 1. Head Of R&D Department 6-Jun-2016 (In Person) 50 mins (Snack foods) 2. Marketing Manager 8-Sept-2016 (Phone) 60 mins 2 EELHT-2 1994 Local Electrical equipment Large High-Tech 1. Company Director 26-Jun-2016 (In Person) 40 mins (Case A) 2. Company CEO 3-Feb-2019 (In Person) 60 mins 3. Production Manager 10-Feb-2019 (In Person) 50 mins 4. Manager of R&D 10-Feb-2019 (In Person) 90 mins Department 3 HELHT-3 1986 International Hardware and Large High-Tech 1. Head Product Development 5-Oct-2016 (Skype) 60 mins Electronics Components 2. R&D Manager 24-Jun-2016 (In Person) 40 mins 4 HSMELMT-4 2006 Local Healthcare Products SME LMT 1. CEO 24-Jun-2016 (In Person) 45 mins 5 ESMEHT-5 2004 International Electronics Components SME High-Tech 1. Managing Director 15-Sept-2016 (Skype) 90 mins (Case B) and GPS 2. Head of Product Development 15-Feb-2019 (In Person) 30 mins 3. Production Manager 15-Feb-2019 (In Person) 70 mins 6 TELHT-6 1946 Local Telecommunications Large High-Tech 1. Head Of R&D Department 7-Jun-2016 (In Person) 60 mins company 2. R&D Manager 7-Jun-2016 (In Person) 50 mins 7 LFSMELMT-7 2015 International Lean-fabrication SME LMT 1. General Manager 2-Jun-2016 (In Person) 75 mins 8 HELHT-8 2004 International Hardware and Large High-Tech 1. Senior Manager of R&D 20-Jun-2016 (In Person) 50 mins Electronics Components Department 2. Production Manager 12-Sept-2016 (Phone) 40 mins 9 SCSMEHT-9 1999 International Semiconductor SME High-Tech 1. Head Product Development 11-Sept-2016 (Skype) 40 mins 10 CLHT-10 1972 International Computer Large High-Tech 1. Head Of R&D Department 11-Jun-2016 (In Person) 60 mins 2. Marketing Manager 11-Jun-2016 (In Person) 45 mins 11 ULLMT-11 1972 Local University Large LMT 1. Head Of R&D and Innovation 12-Jun-2016 (In Person) 60 mins Department 12 FCLLMT-12 1999 Local Furniture and Chairs Large LMT 1. Company Director 25-Jun-2016 (In Person) 50 mins 2. Production Manager 30-Jan-2019 (In Person) 30 mins 13 ITCSMELMT-13 1992 Local IT Consultant SME LMT 1. Regional Director 25-Oct-2016 (Skype) 45 mins 14 ITCSMELMT14 2011 Local IT Consultant SME LMT 1. CEO 20-Jun-2016 (In Person) 60 mins 15 FFSMELMT-15 1988 Local Furniture Locks and SME LMT 1. General Manager 15-Oct-2016 (Skype) 90 mins (Case C) Fittings 2. Production Manager 20-Feb-2019 (In Person) 50 mins 3. Marketing Manager 21-Feb-2019 (In Person) 70 mins 16 Policy Maker-1 - Local Government - - 1. Senior Manager 10-Jun-2016 (In Person) 60 mins 17 Policy Maker-2 - Local Government - - 1. General Manager 10-Jun-2016 (In Person) 50 mins *The shaded rows are the three case studies present in this thesis later (Chapter 7)
123 4.5.2.2 Semi-structured interview strategy
4.5.2.2.1 Interview questions and interview protocol
All the interview questions are developed based on the innovation management, TC and IOC literature, and also refer to the research framework. The full set of interview questions attached in Appendix 1 and Appendix 2. Different interviews protocol followed for Heads of
R&D Departments and CEO / Company Directors (Appendix 1) and Policy Makers and
Government Authority (Appendix 2).
4.5.2.2.2 Selecting interviewees
Thirty interviews were held, with, individuals from Malaysian manufacturing firms, including the Head of R&D Department, Company Director, Managing Director, Regional
Director, Head of Product Development, and Chief Executive Officer (CEO) and two policymakers. All the participants were contacted to agree on the time and venue for face-to- face interviews, or interviews conducted via phone or Skype. Snowballing or referral methods are used to locate other potential interviewees. Through this method participated to have the referral to another participated contact within a similar background or their collaboration partners. A good protocol with Senior Managers and Head of R&D department in Malaysia from selective firms has been developed.
4.5.2.2.3 Conducting pilot interviews or pre-testing the interview
There are main two languages used during the interviews with participants in Malaysia.
English and Malay (National language of Malaysia) were used during the meeting with respondents. A pilot or pre-testing interview conducted before the start of the formal interviews, to ensure the quality and intelligibility of the questions and remove any biases (Su and Parham, 2002; Teddlie and Tashakkori, 2003). The pilot test was conducted with two
124 heads of R&D and one CEO and timed. The expected duration of each interview was from 30 to 90 minutes. After the pilot test, the formal interview was conducted selected respondents for the data analysis.
4.5.2.2.4 Recording the information
The interviews took place in 2016 and 2019, and all were digitally recorded; complete transcripts of the taped data were then prepared. To have a comprehension with data, all the information gathered from the interviews was carefully examined. The data recorded carefully analyzed and examine more than once, this is because to have a comprehensive with the data and to identify the main data content.
4.5.2.2.5 Confidentiality and recording the interview
Before the interview was conducted, a proper email has been sent out to all the participants about basic information about the research and the purpose of the interviews and the expected amount of time involved. Confidentiality issues were addressed and permission obtained from participants to record all the conversation in audio format. Each of the interview statements was allocated to the appropriate category in the conceptual framework: (1) new or significant improvement in products/services/processes (TC building); (2) IOC with external partners for TC building (suppliers, customers, competitors, consultants, private R&D, universities and government research institutes); and (3) Malaysian innovation system- or innovation policy that related to TC building.
4.5.3. Qualitative phase data analysis
Qualitative data analysis is divided into two-phase of analysis. The first phase of data analysis based on 30 interviews from 15 manufacturing firms and two policy-makers (more
125 details discussed in the following section and Chapter 7). The second phase of data analysis emphasis on three case studies, which developed from three individual firms (more details of case studies discussed in section 4.5.3.1 and Chapter 7).
Qualitative phase focuses on how the raw data obtained are analyzed. The methods of analysis include content, descriptive, narrative, interpretive and thematic analysis (Teddlie and Tashakkori, 2003). For this study, thematic qualitative analysis was chosen, widely used for interview data. According to Braun and Clarke (2006), this method is used for identifying, analyzing, and reporting patterns within the data collected. It also provides insight to answer a specific research question. Creswell (2007) and Miles and Huberman (1984), identified three phases of analyzing the data collected through interviews: prepare the data for analysis by transcribing, reduce the data into themes via a coding process and represent the data. This involves familiarization within data, coding the data, theme development, review and naming the themes, and prepare a report. NVivo software was used in this phase to analyze the interviews and manage a large amount of data efficiently and quickly.
First, familiarization with the raw data collected through transcription and translation of the interviews was achieved by listening several times to the 30 interviews, to ensure accurate transcription. The interviews recorded in Malay were translated into English. Then, all the raw data recorded was transcribed using Microsoft Word Office. The raw data recorded are transcribed by classified based on the interview question, then entered on an Excel spreadsheet.
Secondly, all the transcripts and audio recordings were transferred into NVivo software for the initial coding of the raw data. This software enabled sorting and organizing a large set of
126 data, providing in-depth analysis. The coding process followed the conceptual framework and research questions. Find out the content of the data and how it can be related to the research questions. The resulting codes are primary elements of the raw data and identify a semantic content responsive for analysis.
After initial coding of the raw data, potential themes can be identified, initially as significant, broader patterns of meaning. Since the semi-structured interviews and three case studies used for triangulation, the main themes and sub-themes were recognised before the results, to draw a comparison between quantitative and qualitative approaches. Any unnecessary data from the interviews could be eliminated at this stage. NVivo counted all the sources referring to each category and sub-category.
All the themes and sub-themes identified were reviewed to ensure their accurate with supporting evidence, and also to avoid using irrelevant data in the analysis. Finally, a report was written as part of the final review of the qualitative data based on interview findings and analysis of secondary data such as company annual reports, industry websites, industry journals, newspapers, business magazines, and industry association publications. All the rational, coherent and logical data and several themes relating to the manufacturing firms’ TC building via IOC in Malaysia will be discussed in the findings and discussion section of the thesis.
4.5.3.1 Formation of Case Studies
A case study is a research strategy and an empirical inquiry that investigates a contemporary phenomenon, focusing on the dynamics of the case, within its real-life context (Roth 1999;
Yin 2003). Generally, case studies based on an in-depth examination of a single individual or
127 company, group or event to explore the causes of underlying principles. It is a method used to narrow down from a broad field of research into one easily researchable topic and also useful for testing whether scientific theories and models work in the real world.
The main reason to deployed case study strategy in this thesis to show how Malaysian manufacturing firms are implementing collaborations when dealing with the issue of building technological capability via depth or breadth of partner relations. The three mini case studies provided a comprehensive picture of how individual firms building their TC through external collaboration, for example; how they collaborate, who do they collaborate with, and why they collaborate with particular partners, and lastly how the firms used collaboration to build TC.
The three case studies added to ensure better and effective triangulation to meet the research objectives and problems. Out of the total 15 firms considered, three firms selected for the case study analysis. Of the three firms chosen for case studies, the companies classified from different aspect by considering the firm size, two are categorized as SME firms, and one is
Large Firm.
Moreover, out of the three selected firms, two firms were Electrical equipment companies and the remaining one firm is a furniture company. Based on origin, two firms are local firms
(Malaysian) and the remaining one is International firm, and based on technology use consideration, two are high-tech firms and the other one is low-medium tech firm. The following table presented the details about the three case studies firms. The case studies analysis presented in Chapter 7.
128 Table 4.9: Profile of Firms Interviewed for Case Studies. Company Year Local/ Sector Size High-Tech Interviewees Date/mode of Length of Code Established Inter /LMT interview Interview EELHT-2 1994 Local Electrical Large High-Tech 1. Company Director 26-Jun-2016 40 mins (Case A) equipment (In Person) 2. Company CEO 3-Feb-2019 60 mins (In Person) 3. Production Manager 10-Feb-2019 50 mins (In Person) 4. Manager of R&D 10-Feb-2019 90 mins Department (In Person) ESMEHT-5 2004 Inter Electronics SME High-Tech 1. Managing Director 15-Sept-2016 90 mins (Case B) Components (Skype) and GPS 2. Head of Product 15-Feb-2019 30 mins Development (In Person) 3. Production Manager 15-Feb-2019 70 mins (In Person) FFSMELMT-15 1988 Local Furniture SME LMT 1. General Manager 15-Oct-2016 90 mins (Case C) Locks and (Skype) Fittings 2. Production Manager 20-Feb-2019 50 mins (In Person) 3. Marketing Manager 21-Feb-2019 70 mins (In Person)
4.6. Chapter Summary
Overall, this chapter has provided a view of the research methodology adopted in this thesis.
The methods accepted by the researcher used to guide the research objective, problem, and research questions, which identified in Chapter 2. This thesis seeks to understand and find answers on, does emerging economies firms develop TC through external collaboration, specifically focus on manufacturing firms in Malaysia. This chapter has enabled to insight on how the method adopted to underline the research process.
A mixed-methods approach was adopted for research data collection and analysis, with complementary quantitative and qualitative approaches to answer the research objectives and fill the gap identified in chapter two. Therefore, the data collection and data analysis process for both qualitative and qualitative methods demonstrated separately. For the quantitative approach, the survey data adopted from the Malaysian National Survey of Innovation sixth series (manufacturing firm-level data). The qualitative approach, the data collected from semi-structured interviews with selected senior managers of manufacturing firms and policy- makers.
129
Further, three case studies were developed based on three individual firms to show how firms are implementing collaborations when dealing with the issue of building technological capability via depth or breadth of partner relations. Lastly, the findings from both quantitative and qualitative analysis were carefully triangulated along with secondary data (such as company annual reports, industry websites, association publications, newspapers, and business magazines) to achieve the overall research objectives.
130 CHAPTER 5: AN OVERVIEW OF MALAYSIA’S INNOVATION POLICY AND NATIONAL INNOVATION SYSTEM (NIS)
5.1 Introduction
The previous three chapters of this study discussed the literature review (Chapter 2), which explored the nature of TC development and IOC in emerging economies (whereby the main research questions of the thesis identified). While Chapter 3 puts forward the theoretical foundation linking the TC and IOC literature and identifies the research hypotheses. Chapter
4 deals with the research methodology to answer the research questions. This chapter is concerned with Malaysia’s innovation policy and the key actors of the Malaysia national innovation system, as related to TC building and IOC.
The TC literature argues that government strategies and policies are essential determinants of
TC building, especially in emerging economies (e.g. see Katz, 1984; Lall, 1992; Kim and
Nelson, 2000). Lall (1992: 169) stated that “countries - developing or developed - differ in their ability to utilize or innovate technologies, which manifests itself in their productivity, growth or trade performance”. However, in developing countries, government policies play a significant part. For example, the experience of newly industrializing countries (NICs) of
South-East Asia (Singapore, Taiwan, Hong-Kong and Korea) revealed that government policies and strategies play an important role in building up TC, a major factor in their export growth and technology upgrading (Hobday, 1994, 1995; Pietrobelli, 1998; Wignaraja, 2001;
Tsai, 2004) 28 . Government policies and initiatives not only enhance the progress and development of national-level TC building, but also of firm-level TC development (see Bell and Pavitt, 1993; Kim, 1997). Furthermore, government involvement and policies are essential in promoting TC development among firms by providing substantial support
28 For example, the Korean government provide numbers of supports, incentive schemes (Technology Development Reserve) funds, tax credits for R&D expenditures, upgrading human capital and R&D facilities, and grants & long-term low-interest loans for SMEs (Kim, 1993; Lall, 1995).
131 through grants and financial loans, tax incentives, and tax credits for R&D expenditure, and also promoting the research and education infrastructure.
Although this study focuses on firm-level TC development, national policies have to be considered to broaden it understands in the Malaysia context. Malaysia is one of the fastest- growing economies in Asia, with robust growth in the gross domestic product (GDP) exceeding 7% per year over the past three decades (OECD, 2016). Malaysia is also known as the new Asian tiger (second-tier newly industrializing countries) along with Indonesia,
Thailand, and the Philippines (Felker and Jomo, 1999; Wong, 1999; Lall, 2000). Government policies and strategies play a significant role in those countries’ technologies advancement and capabilities for industrial development and economic growth. Therefore, a detailed analysis of innovation policy in Malaysia is crucial for understanding the relationship between formal IOC and TC development at the firm level. In particular, this research examines the relationship between different IOC partners and TC building in manufacturing firms in Malaysia. The role of government support for innovation systems discussed as follows: Section 5.2.1: the evolution of industrial policies in Malaysia; and Section 5.2.2: the emergence of science, technology, and innovation (STI) policy.
5.2 Industry Landscape of the Manufacturing Firms in Malaysia.
Malaysia’s aspiration to become a high-income nation by 2025, the manufacturing sector remains a core sector for economic growth (MITI 2018). The manufacturing industry played a significant part in the economic transformation in Malaysia. Malaysia has remained to attract considerable investments in the manufacturing industry despite a challenging economic environment due to its highly-diversified economy, strong manufacturing
132 foundation, developed infrastructure and connectivity, proactive government policies, and hardworking workforce.
According to the Annual Economic Survey 2018, the manufacturing sector gross sales output recorded a steady growth of 5.7% per year, rising to RM1,275.8 billion compared to
RM1,142.0 billion in 2015. Electrical and electronics (E&E) sub-sector accounted for the highest sales of gross output with RM361.8 billion (28.4%), followed by petroleum, chemical, rubber, and plastic products with RM340.4 billion (26.7%) and the vegetable and animal oils & fats and processed foods with RM214.0 billion (16.8%). These three sub- sectors contributed 71.9% to the sales value of the manufacturing sector, as of 2018 (AES,
2018). The non-metallic mineral, basic metal & fabricated metal products, and textiles, wearing apparel, leather products & footwear are the other two important sub-sectors that contribute over 10.0%.
Manufactured goods continued to dominate both exports and imports of the country. From total export in the year 2018, manufactured goods contributed around 83.3% (RM835.61 billion), which rosed 9.3% from the previous year (RM765.86 billion @ 81.9%). Electrical and electronic (E&E) products held the highest portion of Malaysia’s exports at 38.1%, which increased by 11% to reach RM380.82 billion. Other manufactured sub-sectors contributed to the growth of exports in 2018, which comprise of petroleum, chemicals and chemical products, manufactures of metal, machinery, equipment and parts as well as optical and scientific equipment. On the other hand, imports of manufactured goods accounted for
87% of Malaysia’s total imports in 2018 compared to 87.2% in 2017. The E&E products are the major imports of manufactured goods in 2018, followed by petroleum products,
133 chemicals and chemical products, machinery, equipment and parts as well as manufactures of metal. These five sub-sectors estimated at 62.7% in 2018, which same in 2017 as well.
The focus on SMEs in recent years is bearing fruit as the sector’s contribution to the
Malaysia’s GDP grew by 38.3% to RM521.7mil in 2018 from RM491.2mil in 2017. SME
Corporation Malaysia said the growth in the sector’s contribution to the GDP outperformed the overall GDP growth of 4.7% in 2018. In the manufacturing industry, the SME value- added expanded by 5.5%, led by non-metallic mineral products, basic metal and fabricated metal products as well as petroleum, chemical, rubber and plastic products. On exports, SME
Corp said SMEs recorded a 3.4% growth in 2018, supported notably by manufactured goods and chemicals products. In terms of value, SME exports increased to RM171.9bil in 2018 from RM166.2bil in the previous year, while its share of overall exports remained at 17.3%.
The Malaysian government will formulate several key strategies for strengthening the export capacity and capability of SMEs under National Policy on Industry 4.0 (for more details refer to Section 5.3.3). According to SME Corporation Malaysia, in the year 2019, eight broad measures were endorsed by the National Entrepreneur and SME Development Council to boost SME contribution towards economic growth. Those measures comprise of intensifying digitalization, connectivity and cybersecurity among SMEs, enhancing SMEs integration in the supply chain, supporting more high growth SMEs with technology-driven. The National
Policy on Industry 4.0 purely focuses on the manufacturing sector and related services, including manufacturing SMEs seen as core sector to achieve developed-nation goal by the year 2025.
134 5.3 Malaysian’s Innovation Policies
The Malaysian economy has experienced remarkable growth and also undergone a structural transformation over recent decades. The economy has transformed from being largely agriculture-based economy to one focused on the manufacturing industry, including heavy industry. Since 1990, the Malaysian government has focused on developing a diversified and knowledge-intensive or innovation-driven economy. This policy commitment actually started in the early 1970s, during which Malaysia’s National Pillars or “Rukun Negara” were developed, and increasingly acknowledged under five-year national development plans known as Malaysia Plans (Rahman, 2013; OECD, 2016). In line with the growing interest in
STI policy and the driver of STI activities became prominent in these plans and strategies in the mid-1980s. The Malaysian government started to recognize the “innovation imperative”, collaboration for innovation and TC development as critical assets for industrial development, to move up the value-added ladder in global value chains that enables countries to compete globally to achieve the status of a high-income nation (Lall, 1994; World Bank,
2014; OECD, 2016). Since the late 1980s, STI policy had always been an integral part of the various development plans, as illustrated in Table 5.1.
In the 1960s, Malaysia started to follow the import-substitution development strategy, progressively shifting the focuses toward an export-oriented industrialization strategy in the late 1970s. The major transition of this strategy was to attract foreign direct investment (FDI) for sustainable economic growth through industrialization. Thus, legislation such as the
Companies Act of 1965 (CA) and the Investment Incentive Act of 1968 introduced to attract foreign participation to make a considerable capital investment in both the manufacturing and services sectors. In 1980, Malaysia aggressively pursued the second-order import-substitution strategy through the Heavy Industries Corporation of Malaysia (HICOM) with substantial
135 government backing to develop heavy industries such as cars, cement, and steel. Malaysian government started to recognize the importance of TC development for country advancement and sustainable growth. Since then, the government progressively put more emphasis on external collaboration and TC development.
Other emerging economies like Thailand, Indonesia, Philippines, Argentina, Chile,
Colombia, Costa Rica, Panama, and Uruguay have followed similar strategies for industrial transformation (e.g. see Bell and Pavitt, 1993; Smith, 2005; Felker and Jomo, 1999; Lall,
2000; Crespi and Zuniga, 2012; OECD, 2016), but without an explicit STI policy and therefore achieving only limited economic development (e.g. Lall, 1992; Bell and Pavitt,
1993, 1995). The Malaysian government acknowledged the weaknesses of strategies which slow down economic growth and the progress of industrialization and recognized the need for
STI policies for national and firm-level TC development (EPU, 1986, 1990).
During the 1980s, the Malaysian government, therefore, started to stress the importance of
TC building for industrial development and thus attains a reasonable level of economic progress (EPU, 1996; PEMANDU, 2014). In 1986 it introduced the First National S&T
Policy, whose first chapter dedicated to STI sectors. Several initiatives were introduced directed at TC development. These included growth strategies on modernization and industrialization that focused on new and emerging technologies (like ICT), as in the
Industrial Master Plan 2 and the announcement of “Vision 2020”. A detailed analysis of the early industrial policies, Industrial Master Plan (IMP), and the emergence of STI policy related to TC development are discussed in the sections below.
136 Table 5.1: Malaysia’s Science, Technology and Innovation (STI) development stages from 1960 to 2015. 1960 1970 1980 1990 2000 2010 2015 Population 8.1 million 10.9 million 13.8 million 18.1 million 23.3 million 28.3 million >30.7 million GDP (at current USD) USD 2.4 billion USD 4.3 billion USD 24.9 billion USD 44 billion USD 93.8 billion USD 192.8 billion USD 313 billion R&D budget as a % of - - - 0.22 0.47 1.07 1.13 GDP Development stage of the Primary commodities, agriculture, provision of basic Investment-driven stage; shift to manufacturing; focus National Innovation infrastructure as well as developing operational on learning as well as developing duplicative Focused on knowledge-based/innovation economy Strategy capabilities imitation and adaptive capabilities Major industrial policy Heavy dependence on Move from net oil importer Regulatory reforms that led Growth strategies Focus on productivity- driven Greater emphasis on Focus on increasing direction primary export to exporter as petroleum to more liberalised private favouring growth; stimulating knowledge-based, manufacturing value added, commodities; decline of prices rose sharply; free sector investment; gradual modernisation/ knowledge-based indigenous innovative economic down-streaming activities, rubber prices; beginning of trade zones attracting shift to heavy industries; industrialisation, shift innovation; Industrial Master growth indigenous innovation import substitutions multinational companies; Industrial Master Plan 1 to new and emerging Plan 3 (2006-20); capacity and capability; and export- led (1986-95) technologies e.g. ICT; Knowledge-based Economy global market access industrialisation Industrial Master Plan Master Plan 2 (1996-2005); promotion of clusters STI policy and role of Limited focus Dedicated Ministry for 1st National STI Policy; Multimedia Super 2nd National STI Policy; Year of Innovation; Science to Action (S2A): government Science established as well first chapter on STI in Corridor established; National Innovation Council; Talent Corporation Mainstreaming STI – as the National Council for Malaysia plans; National IT Council; Biotech Strategy announced; established; UNIK, raising the profile of STI Scientific Research and Intensification of Research mega-projects era; IRPAs streamlined; Brain Performance and infusing STI into nation Development in Priority Areas (IRPA) Returning Scientist Gain Programme launched Management and building; National Policy grants; double-deduction Programme Delivery Unit on Science, Technology & incentives for R&D Innovation (2013-20) Macroeconomic policy First Malaysia Plan (1966- New economic policy Large investments in heavy Vision 2020 National Economic Advisory New Economic Last leg towards Vision framework/conditions 70) launched; substantial focused on national unity, industries; significant growth announced; Action Council, National Innovation Model; Tenth 2020 – Eleventh Malaysia increases in public sector restructuring society for in foreign direct investment; Plan for Industrial Model; second phase of Malaysia Plan (2011- Plan (2016-20); final phase expenditure greater Malay urbanisation major recession in mid- Technology Vision 2020, focused on key 15) followed by the of the Economic and employment 1980s Development; Asian strategic thrusts for Economic Transformation Program; economic crisis sustainable growth Transformation implementation of Goods Program; global and Services Tax (GST) economic crisis Education policy Becomes federal Focus on improving Continued focus on Rapid transformation/ Ministry of Higher Education Science and maths to Malaysia Education responsibility; focus on quality; system begins improving quality and reform; opening of established; National Higher be taught in Bahasa Blueprint 2015-2025 basic education for all adjusting to economic access; National Vocational private sector Education Action Plan; Malaysia (the official (Higher Education),1 whose needs Training Council institutions; Human creation of research language of Malaysia) main aim is to produce Resource universities; APEX from 2012 holistic and balanced Development Fund university; University graduates with an Grading System; entrepreneurial mind implementation of Malaysian Qualifications Framework; National Dual Training System Sources: Thiruchelvam et al., 2011: 5-6; Thiruchelvam et al., 2013: 60; OECD, 2016: 153-154.
137 5.3.1 The evolution of industrial policies in Malaysia
A few years after independence in 1957, if the World Bank’s “country’s income” classification system had existed, Malaysia would have been classified as a middle-income nation (Chee, 1987). Since then, it has continued to enjoy relative prosperity as a primary commodities exporter of tin and rubber. Malaysia accounted for half the world’s tin production in the 19th century (Wong, 1999; Thiruchelvam et al., 2011; OECD, 2016), and the rubber sector has also been a major contributor to the during the 20th century. These two sectors represented 85% of total exports and 48% of gross domestic product (GDP) of
(MIGHT, 2009, 2013; OECD, 2013).
However, the limitations of significant dependence on the export of resource-based industries were the major reasons for the Malaysian government to shift the focus to the manufacturing sector. As early as 1958, the government’s industrialization strategy started with the introduction of the Pioneer Industrial Ordinance (PIO) to provide fiscal incentives for industrial investments, complemented by protectionist measures and the first initiatives to attract foreign firms, principally through the improvement of infrastructures (Chee, 1987;
Rasiah, 1995). This initiative was followed by the introduction of tariffs and quotas in 1960 to support the growth of an import-substitution industrialization strategy. These policies aimed to encourage the production of Malaysian soil by local and overseas firms of items imported from other countries in an attempt to protect infant industries.
In 1960 the Malaysian Industrial Development Finance (MIDF)29 was established, and in
1965 the Malaysian Industrial Development Authority (MIDA) was set up to provide financial aid and other support for investors in manufacturing industries (Chee, 1987). The
29 MIDF an institution and the main objective to promote the development of the manufacturing sector through long and medium-term business loans for SMEs (http://www.midf.com.my).
138
government also started to engage more actively, both directly and indirectly in the industrialization process by channeling more financial support into the manufacturing sectors.
A few foreign companies established operations in manufacturing-related businesses in
Malaysia, such as Matsushita Electric in 1965, which previously only had a small-scale trading firm that supplied domestic markets (OECD, 2016: 148). A considerable number of initiatives were included under the Investment Incentive Act of 1968 for firms to invest in new business or expand existing ones to attract more foreign investment companies (Yusoff et al., 2000). These incentives included total or partial relief from income tax, locational incentives to disperse industries, and labour utilization relief aimed at encouraging labour- intensive assembly operations in Malaysia.
In the 1970s, Malaysia began an export-oriented industrialization strategy, and this period witnessed aggressive efforts by the government to attract FDI to spur the nation’s industrialization efforts. Generous government incentives such as tax relief and subsidized investment loans were provided to attract FDIs in free trade and export processing zones.
These incentives succeeded in attracting many multinational companies (MNCs) to locate their investments in Malaysia (Felker and Jomo, 1999; Thiruchelvam et al., 2011; OECD,
2013b). The manufacturing industry, primarily non-resource based, developed, and matured; for example, the electrical and electronics industry (E&E) showed significant growth (e.g. see
Hobday, 1994; Raghavendra and Subrahmanya, 2006).
The first free trade zones were introduced in 1972, paving the way for export-oriented E&E related firms to start up their production lines in Malaysia (see Hobday, 1995; Wong, 1999).
The Industrial Coordination Act followed in 1975 to speed up the pace of manufacturing industry growth and to accomplish the export-oriented industrialization strategy goals. The
139
generous investment incentives resulted in attracting the giant consumer electronics companies and soon followed by hardware companies from the United States like Intel,
Freescale Semiconductors, and Dell (Felker and Jomo, 1999; Rasiah, 2006; OECD, 2016).
Whether the changes in industrial policy influenced growth is debatable, but the Malaysian manufacturing sector grew to represent 12% of GDP in the 1980s compared to 6% average annual growth in the 1960s and 7.5% in the 1970s (Chee, 1982, 1987). The nascent E&E companies specialized in semiconductors, the center of attention for labour-intensive assembly operations with almost no involvement from local companies (PEMANDU, 2012).
In the early 1980s, the Malaysian government introduced the second-order import- substitution strategy through the Heavy Industries Corporation of Malaysia (HICOM), as well as continuing to attract FDIs (see Rasiah, 1995; Yusoff et al., 2000; Thiruchelvam et al.,
2011). This focused on large-scale and capital-intensive projects in heavy industries such as steel production, cement, machinery and equipment, petrochemicals, and domestic automobile manufacturing. The government offered subsidies such as financial support and also imposed close controls on competitors in the domestic market; it developed other initiatives aimed at acquiring foreign technologies through the collaboration for growth of technological development in the manufacturing sector. For example, the first Malaysia national automotive project (Proton) was formulated from a joint venture between HICOM and Mitsubishi Motor Corporation, where the engine and gearbox technologies and expertise were transferred from Japan to Malaysia (Rasiah, 1995; Wad and Chandran, 2011). Special attention was paid to the development of a strong capital goods sector and interrelationship with the domestic enterprises, particularly those involving Bumiputera30 (indigenous Malay) owners. Rasiah (1995: 107) stated: “Indeed by controlling Proton’s purchases through direct
30 Bumiputera is the Malaysian term to describe the Malay race and other indigenous peoples of Southeast Asia and also known as “son of the soil”. 140
ownership, the government has been gradually enforcing domestication and Bumiputera participation through their umbrella concept of vendor development”.
5.3.1.1 Malaysia Industrial Master Plans (IMPs)
The First Industrial Master Plan (IMP1) was launched in 1986 by the Ministry of
International Trade and Industry (MITI, 1986). This plan was to guide the development of the manufacturing sector from 1986 to 1995. IMP1 provided a framework for diversifying and integrating manufacturing industry. One of its main objectives was to focus on developing the manufacturing sector to leapfrog towards an industrialized nation by building indigenous
TC and competencies. The state continued the export-led industrialization strategy via liberalization of trade and investment, and financial incentives to attract export-oriented firms for higher value-added activities; e.g. tax benefits on companies’ R&D expenses and training
(UNIDO, 2003). IMP1 gave special attention to the 12-targeted sub-sectors, comprising seven resource-based sectors (rubber, wood, palm oil, chemicals and petrochemicals, wood- based, non-ferrous metals, and non-metallic minerals) and five non-resource based sectors
(E&E, transport equipment, machinery and engineering, iron and steel, and textiles).
Several new tax incentives were provided under the Promotion of Investments Act, 1986, which replaced the Investment Incentives Act, of 1968 for investment in manufacturing, agriculture, and tourism. Special incentives were channeled towards export enlargement, and the growth of SMEs is crucial to developing inter-industry relationships. The first IMP also emphasized the significance of science and technology and human resource support for the acceleration of industrialization. This highlights the importance of the development of the workforce with technical expertise and knowledge to build indigenous capability in product design and production technology. IMP1 was implemented to accelerate the technology
141
transfer activities of the multinational corporation (MNCs) from their home countries to
Malaysia (UNIDO, 1986: 15-24). However, TC development in Malaysia was still primarily constrained by domestic firms’ inability to recognize the overall process of operational practices of MNC subsidiaries’ technology (UNIDO, 1986: 15).
The Action Plan for Industrial Technology Development (APIDT, 1990-2011) was launched to follow IMP1, and set the ground for strategic and integrated steering of innovation activities in specific sectors. This action plan was initiated to increase industrial R&D, supported by greater public resources via financial loans and grants, to reorient part of the activities in existing research laboratories and institutions toward industrial-oriented and market-driven research (EPU, 1990; Raghavendra and Subrahmanya, 2006).
In 1996, the Malaysian government initiated the second Industrial Master Plan (IMP2) from
1996 to 2005. This plan emphasized the development of TC through the inter-organizational linkages and competitiveness in manufacturing sectors. IMP2 was a continuation of the earlier plan but paid significant attention to industrial growth, incorporating both the manufacturing sector and business support services (MITI, 1996). It also aimed to increase firms’ competitiveness at the global level, transforming the manufacturing sector to more automation, with production lines incorporating high technology-based and knowledge- driven processes (MITI, 1996: 63; MITI 2016).
Networks and IOC recognized as essential drivers of Malaysian economic transition (Felker and Jomo, 1999; Wong, 1999; Thiruchelvam et al., 2011). In the second IMP, the industrial development approach shifted from the traditional industry-based strategy to a cluster-based industrial development strategy to strengthen the links between firms and improve the
142
existing industrial structure. The cluster-based plan also aimed to promote SMEs to build business links with MNEs, to develop their TC and further strengthen the inter-organizational relationship at all levels of the value chain (MITI, 1996: 30).
The focus of manufacturing sectors under the new growth strategy moved from assembly- intensive to an integrated, industry-wide approach for both manufacturing and business support services. In line with this, dubbed manufacturing ++ or manufacturing-plus-plus, the modified strategy set out the framework for industrial development. It involved changing the industrial structure from predominantly basic assembly and production operations into more upstream activities such as research and design and product development, as well as downstream activities such as distribution and marketing. The objective was to move into higher value-added activities. The strategy entailed not only moving along the value chain but, more importantly, shifting the value chain upwards through productivity growth. The
Multimedia Super Corridor (MSC) can be seen as an important initiative that accounted for world-class infrastructure (as a high-technology center); its hub was located in Cyberjaya to attract world-class technology companies (e.g. leading E&E companies like Intel, Dell, and
IBM).
The Third Industrial Master Plan (IMP3) was introduced in 2006 and outlined the industrial strategies and policies that Malaysia intended to focus on between 2006 and 2020 in order to fulfill the country’s aim of achieving the status of an industrialized country, as stated in
Vision 2020. This plan was also initiated partly to address the weaknesses of the first and second IMPs; and slow down the activities in the manufacturing sector by decreasing its production and exports (Rasiah, 1995; Yusoff et al., 2000). Instead, the government shifted it attention to the service sector, which it perceived as a major source that would drive
143
economic growth between 2006 and 2020. This would anticipate the possibility of premature de-industrialization in Malaysia (Basu Das and Lee, 2014; OECD, 2016). A similar problem was experienced in other developing and emerging economies, such as Latin American countries, because of the significant decline in the manufacturing sector resulting from various industrial strategies.
IMP3 is a 15-year blueprint perceived as a key to driving industrialization towards a higher level of international competitiveness through innovation and TC building, by the transformation of both manufacturing and service industries to accomplish the status of fully developed country by 2020. It emphasized that: “necessary measures are undertaken in critical areas, in particular, industry restructuring and transformation, technological upgrading and innovation, and greater integration of domestic companies into regional and global networks and supply chains” (MITI, 2016: 42).
5.3.2 The emergence of a science, technology & innovation (STI) policy
The government started to recognize the importance of a national innovation system (NIS) in its policymaking in the 1980s (Thiruchelvam et al., 2011; Rasiah, 1999) and attempted to better coordinate its STI policies for TC building and sustainable economic growth. Table 5.2 presents an overview of various significant initiatives introduced by the government from
1980 to 2010. The rationale behind these initiatives was not only to enhance the efficiency of the R&D public scheme, but at the same time enable more effective R&D allocation in line with the country’s industrial development and TC building. Several research institutions were established in the 1970s and 1980s to direct the nation’s attention to targeted economic sectors like tropical medicine, and rubber and wood-based industries. Over the years, more
144
R&D institutions followed, primarily focusing on science and technology-based industries.
The historical development of Malaysia’s STI policies and NIS sketched out in Table 5.1.
In 1973, the government established the Ministry of Energy, Technology, and Research
(METR) as the agency accountable for planning, promoting, and organizing all the science and technology-related activities of the whole nation (Thiruchelvam et al., 2013). The
Ministry’s responsibility was later taken over by the introduction of the National Council for
Scientific Research and Development (NCSRD) in 1975, which was also in charge of advising the government on scientific and technological affairs. However, NCSRD was not able to perform the task entirely of promoting the country’s STI orientation and development due to some critical challenges (EPU, 1986, 2001, 2013; OECD, 2016).
The Office of Science Advisor was introduced in 1984 to focus on STI policies. The Office to provide extensive advice on STI policy-related issues and reported directly to the Prime
Minister. The introduction of this Office was the key influential factor for the establishment of the First National Science and Technology Policy (NSTP1) under the Ministry of Science,
Technology, and Environment (MOSTE). The Ministry established in 1973, in 2004 became the Ministry of Science, Technology, and Innovation (MOSTI). The first NSTP set out various strategic plans and a framework to encourage STI self-reliance and STI information development, and also to promote the private sector’s involvement in scientific and technological activities in line with national industrial development (EPU, 1990;
Thiruchelvam et al., 2011).
The 5th Malaysia Plan (1986-1990) witnessed a critical footstep toward the institutionalization of STI policy with the first section dedicated to STI. However, no specific
145
financial aid or grants allocated for STI development. The first section on STI reflected the initial stage of comprehensive and integrated orientation relating to public R&D expenditure and also included a specific line under the national five-year budget allocation for STI activities (EPU, 1990; Kanapathy, 2000). The Research and Development Fund was launched during the 5th Malaysia Plan to provide financial aid and grants to initiate all the projects under STI public policy. Further, the government introduced the Malaysia Industry-
Government Group for High Technology (MIGHT) and Malaysia Business Council (MBC) to promote high-tech industry investment consortia and to accelerate the linkages between industries, respectively.
The extensive efforts by the government on STI policy led to significant development in new and emerging technologies, especially ICT, specifically the establishment of the Multimedia
Super Corridor (MSC) for continuous scientific and technological development to meet national industrial goals. Following the significant changes in both global economic and technological evolution, the Second National STI Policy (NSTP2, 2002-10) was launched in
2003 by MOSTE. It emphasized several key issues related to STI: strengthening R&D and
TC development, promoting research commercialization to research awareness, encouraging a culture of science, innovation and techno-entrepreneurship for SMEs, developing a stronger institutional framework and management for STI, and monitoring STI policy implementation and developing competence for specialization in key emerging technologies (MOSTI, 2003,
2009, 2012, 2013b; EPU, 2006). Another crucial recommendation of NSTP2 was the need for effective monitoring and evolution following the failure of earlier plans.
146
Table 5.2: The STI initiatives in Malaysia between 1985 and 2010. Main governance Main initiatives fully or partially implemented functions Policy advice and § Strengthen of the National Council for Scientific Research and Development (NCSRD)1 to allow intersectoral steering co-ordination (1986-90) § Transfer of the responsibilities for technology transfer from the Co-ordinating Council for Industrial Technology Transfer (CCITT, created in 1982) to the NCSRD (1985) § Establishment of a higher level Cabinet Committee chaired by the Prime Minister to authorise STI-related legislation and programmes (1991-95) § Review of the role and governance of the NCRSD to allow it to perform effectively its role of an STI advisory and co-ordination system (1996-2000) § Creation of the National Innovation Council (2004) to advise on STI policy and devise key strategies to stimulate innovation Policy formulation § Launch of the First National STI Policy (1986-89) § Launch of the Second National STI Policy (2002-10) § Distinct STI chapter in the national development plans for the first time in the Fifth Malaysia Plan (1986-90) Policy implementation § Creation of a National Innovation Implementation Co-ordination Committee to oversee the implementation of resolutions from the National Innovation Council (2006) § Creation of a central R&D fund to finance all public support to STI activities § Creation of the Intensification of Research in Priority Areas programme (IRPA) to gather all public R&D funding schemes for higher education institutions and research institutes (not firms) under an integrated allocation and review process (1987) § Revision of the IRPA funding mechanisms to increase effectiveness (2000) § Transfer of the main research institutes under the supervision of the Ministry of Science, Technology and Environment (1991-95) § Creation of new research support schemes to support private companies (Commercialisation of R&D Fund, Technology Acquisition Fund, etc.) § Establishment of the Malaysian Technology Development Corporation to promote the creation and development of technology businesses (1992) Policy information, § Build the infrastructure for STI information gathering and analysis, as well as Malaysian STI performance analysis, monitoring evaluation and monitoring (creation of the Malaysian Science and Technology Information Centre) and evaluation § Creation of the Malaysian Industry-Government Group for High Technology, Prime Minister’s Department (1993) Policy framework § Review and reform of the national intellectual property rights system, legislation, practice and institution § Establishment of the intellectual corporation of Malaysia (Pejabat Cap Dagangan dan Jaminhak/Paten, which then became known as “MyPo”) (2003) Sources: OECD, 2016: 152.
5.3.3 Industry 4WRD: National Policy on Industry 4.0. (Year 2018-2025)
The National Policy on Industry 4.0 known as Industry4WRD was launched by Malaysian
Prime Minister YAB Tun Dr. Mahathir Mohamad on 31 October 2018 (MITI 2018). The
Industry4WRD policy aims to pave the way to transform the manufacturing sector and related services within the period from 2018 to 2025 to achieve developed-nation goal by the year 2025. Further, it would encourage the development of innovative capacity and capability of the manufacturing sector and related services to create Malaysia’s own technologies, products and services.
147
There are four specific goals are established under Industry4WRD: 1. To increase the level of productivity in the manufacturing industry per person from RM106,647 by 30%. 2. To elevate the total contribution of the manufacturing sector to the economy from RM254 billion to RM392 billion. 3. To strengthen our innovation capacity and capability as reflected by improvement in Global Innovation Index ranking from 35th to top 30. 4. To increase the number of high-skilled workers in the manufacturing sector from 18% to 35%.
The Prime Minister of Malaysia YAB Tun Dr. Mahathir bin Mohamad claimed that on 31
October 2018 (MITI, 2018) as:
“Industry4WRD is Malaysia’s response to Industry 4.0, and beyond, that calls for the transformation of the manufacturing sector and its related services to be smarter and stronger, driven by people, process and technology. I believe that the Industry4WRD would enable the manufacturing sector to move into Industry 4.0 and along the way contribute to fulfilling Malaysia’s commitment to the United Nation’s Sustainable Development Goals”.
The Industry4WRD policy was Malaysian initiative to follow other global and leading manufacturing countries that have already embarked on their Industry 4.0 transformation and are in advanced stages of implementation, e.g. Germany, the US, the UK, China and the
Republic of Korea. It is essential for Malaysia to learn from these experiences and move fast in its own Industry 4.0 adoption to transform the manufacturing industry into their future industrial state, and not fall behind in its global manufacturing position.
The Industry4WRD covers catalytic sectors and high growth potential industries identified under the 11th Malaysia Plan are Electrical & Electronics, Machinery & Equipment,
Chemicals, Aerospace and Medical Devices. Special attention was given to SMEs for them to
148
propel forward as they account for the bulk of manufacturing (98.5%) and a significant part of employment (42%). Industry 4.0 will transform and scale-up SMEs to remain competitive by capturing the benefits of disruptive technologies and innovation. The Malaysian government firmly believes that the manufacturing sector is fundamental in turning Malaysia into a major player in the global value chain apart from rapidly turning the country into an industrialized nation by 2025.
5.4 Malaysian Innovation Policies Mix: Key Issues and Problems
The history of Malaysia’s economic development since independence is impressive by any measure, according to the OECD report of 2016. From an agrarian-based economy entirely dependent on primary commodities, the country has been successfully navigated to a multi- sector economy with manufacturing and services, including heavy industry, to fuel economic growth. More recently, the government put more emphasis on developing a knowledge- and innovation-driven economy to achieve the status of the industrialized country and to catch up with other developed countries. Malaysia’s policies and strategies have played a significant role in creating the conditions for this economic transformation, from infrastructure to various projects and tax incentives.
However, Malaysian industrial and STI policies have been criticized by observers and academics for not helping Malaysia attain developed country status as readily as other Asian countries like Singapore and Korea (e.g. see NEAC, 2009; Rasiah, 1999; Felker and Jomo,
1999). Nevertheless, the government played a central role, and its interventions have been instrumental in promoting and guiding national growth, especially its notable economic performance in the 1990s (e.g. see OECD, 2013, 2016). Malaysia recorded its slowest economic progress during the Asian financial crises of 1997 and 2008, and this raised
149
questions about the effectiveness of the current STI policies in upgrading the manufacturing sector (NEAC, 2013; OECED, 2016). According to the New Economic Model (NEM)31, numerous STI policies and strategies implemented by the government for industrial progress and technological development were insufficient to drive the country to the next level of industrial growth (NEAC, 2009; EPU, 2010). Malaysia’s innovation imperative and technological advancement became increasingly less effective and prominent in national growth policies and plans (OECD, 2016; EPU, 2015).
In recent years, the government has recognized the fiscal tightening and responded to the imperative by implementing various strategies and policy initiatives direct and indirect, as well as quantitative and qualitative to enhance innovation activities and TC development.
However, the numerous plans and projects implemented to support the knowledge- and innovation-driven economy have been confronted by ineffectiveness in government, and difficulties in executing reform due to increasingly complex Malaysia NIS (EPU, 2015b;
OECD, 2016). Therefore, this study has identified three major weaknesses of Malaysia’s STI policies mix:
Complex and frequent revisions of the innovation policy system
The Malaysian STI policy mix is increasingly diversified, and the number of new organizations and initiatives related to STI keeps expanding. According to the NSRC
(2013) 32 , the introduction of new policy or strategies in Malaysia often leads to the establishment of more innovation institutions and organizations at both strategic and implementation level. For example, Degelsegger et al. (2014) argued that eight ministries and
31 The New Economic Model (NEM) was launched by Malaysian Prime Minister Najib Razak in 2010 to drive Malaysia’s transformation to become a developed nation by 2020. 32 National Science and Research Council Malaysia (NSRC) was introduced under Malaysia’s 10th Plan to put special attention on R&D priority areas to drive STI development (NSRC, 2013). 150
14 agencies are responsible for providing grants and loans to support R&D activities. Other sources recently suggested that ten Ministries and 44 agencies are now accountable for the acceleration of STI activities (EPU, 2015; OECD, 2016).
The Malaysian innovation policy mix largely characterized by complex and significant levels of functional overlap between institutions and STI authorities related to the implementation of the strategies and plans (Thiruchelvam et al., 2011; EPU, 2015). The channels providing grants and subsidies to support the R&D activities and commercialization are numerous, but the high level of functional overlap is harmful to both public and private sector innovation activities.
The multiplicity of STI actors responsible for similar tasks increases the issues of redundancy and conflict of interest (NSRC, 2013; EPU, 2015). According to a recent report by OECD
(2016), the global experience recommended that a high level of overlap and redundancy between government agencies can enhance the efficiency and effectiveness of projects and the overall STI system. In the case of surplus, the overall strategies tend to reduce the tendency of both private and public industry collaboration in R&D and innovation activities, resulting in inconsistencies and contradictions between the different support agencies and significantly affecting the overall objective of the plans and projects.
Frequently revising the Malaysian STI policy system increases the complexity and decreases the transparency among key actors (Thiruchelvam et al., 2013; World Bank, 2014). Openness between institutions and agencies could improve their prerogatives in innovation policy, leading to the continuous restructuring of the STI policy landscape. Lack of transparency in the STI policy system in Malaysia results in weak management and poor implementation of
151
the strategies and plans, requiring more time to build stronger relationship and the trust of key actors that make clear road maps for the success of the overall STI policy (OECD, 2013,
2016). For instance, from 2005 to 2010, MIGHT33 function under the purview of the Ministry of Science, Technology and Innovation; it was then moved back under the Science Advisor to the Prime Minister. In 2014, it came under direct observation of the Prime Minister’s
Department.
Multiplicity of STI institutions
Contradictory and inconsistent guidance is a significant issue recognized by Malaysian STI authorities (NSRC, 2013; Thiruchelvam et al., 2013). The NSRC was established to provide substantial support for targeted R&D areas (e.g. ICT, medicine & healthcare, biodiversity, food security), while the introduction of the National Innovation Council focused on leadership in developing new STI policies. Several institutions are responsible for STI policy and its implementation. For example, the Global Science and Innovation Advisory Council is accountable for strategic advice on STI-related activities, while the National Science Council and the Global Science and Innovation Advisory Council collaborate to design the STI scheme and programme (EPU, 2015; NSRC, 2013).
The rapid increase in the number of Malaysia STI governance institutions on targeted sectors is the major barrier to an integrated STI policy. There are several major weaknesses relate to sectoral issues and greater proximity to the action, highlighted below (MIGHT, 2013):
v Overlapping tasks and responsibility. v Lack of transparency and synchronization among STI actors. v Limited number of specialists; an adequate mix of people. v Fewer follow-up meetings.
33 MIGHT was established in 1993 as an independent non-profit organization that provides support as a technology think-tank under direct observation of the Prime Minister of Malaysia (http://www.might.org.my/) (see MIGHT, 2009, 2013, 2013b). 152
As pointed out in the last report by OECD (2016), Malaysia’s STI institutions and agencies have limited experience at the international level, in both form and function. This means that they struggle to coordinate the agenda-setting council and implementation of STI activities effectively (e.g. see NSRC, 2013; Yusoff and Pillai, 2014; EPU, 2015). The debates around the coordination of agencies and agenda setting began in 1957. In 1963, the Malaysian government established the Pan Malayan Scientific Advisory governing body, responsible for advising on and coordinating innovation policy matters. Within a few years, the council had become remarkably ineffective due to government failure to take action on its recommendations and was soon defunct. In a second attempt, the government created the
National Council for Scientific Research and Development; this lost its power in a short period of the time and was replaced by the National Science and Research Council (NSRC) in 2011. In 2016, the NSRC became the National Science Council (NSC).
Limited attention has been paid to STI target areas and lack of coordination from the central government on their plans and programmes (Thiruchelvam et al., 2011, 2013; EPU, 2015).
For instance, the Tenth Malaysia Plan identified 11 key areas important for national economic growth, while the Economic Transformation Programme targeted 12 development areas; the Third Industrial Master Plan, 2006-2020, focused on 12 manufacturing industries, and MOSTI concentrated on national technology planning areas under the Mega Science programme. Further, the Prime Minister’s Science to Action plan targeted several priority areas, with Governance for Science and Science for the industry at the top of the list
(MIGHT, 2013; MOE 2015). In order to overcome the multiplicity of priority-setting institutions, the central government should set priorities based on the importance of targeted areas and programmes, similar to what was done by the MOSTI Techno-Fund.
153
Lack of stability and flexibility in coordination
The multiplicity of STI institutions and agencies make coordination more complex and less effective for different strategies and plans (Thiruchelvam, 2013; Rasiah and Chandran, 2015;
EPU, 2015). Several domestic and international actors and researchers have criticized the deficiency of the organization in its responsibility for establishing central coordination between STI institutions and projects (e.g. see MOSTI, 2009, 2012; NSRC, 2013; MIGHT,
2013, 2013b).
The excessive number of ministries and agencies responsible for R&D and STI initiatives has resulted in uneven distribution of resources as well as overlapping and contradictory priorities, and there is almost no central coordination among the institutions (EPU, 2015). For example, the government have initiated various national blueprints to enhance the STI and innovation system, such as NPSTI (2013-20120), the Higher Education Blueprint (2015-
2025) and the SME Master-plan (2012-2020); however, there is a lack of central coordination in implementing these plans (ASM, 2015). According to Rasiah and Chandran (2015), both
MOSTI and Ministry of Education Malaysia (MOE) are the main pillars of the country’s innovation system, with MOSTI responsible for applied research and MOE for basic research; however, there is no clear coordination or unified process for basic and applied research. Thiruchelvam (2013) argued that the limited success of the fiscal incentives and funding programme was due to: 1) poor coordination between numerous STI institutions and initiatives; 2) cumbersome strategies; and 3) restrictive environment and innovation system.
The problem of lack of stability and flexibility in coordination was highlighted in the 10th
Master Plan when the Prime Minister’s Office was appointed the new head and also given a more prominent role in managing and coordinating the overall institutional structure and STI
154
activities. This unit considered as the right fit for the responsibility of dealing with all innovation initiatives across all industries and value chains (NSRC, 2013). The Prime
Minister’s Office introduced the Unit Inovasi Khas to operationalize and drive the STI activities across the nation. However, this unit was later taken over by the National
Innovation Agency of Malaysia, with similar responsibilities. Under the 10th Malaysia Plan, the Prime Minister himself was the chairman of nine STI-related institutions, from biotechnology to brain-technology. The direct intervention of the Prime Minister’s Office and the Prime Minister himself reduced coordination among the STI institutions and strengthened the STI governance silos, resulting in and fragmentation (EPU, 2010, 2015; NSRC, 2013).
The government recently proposed a Central National Research Agency for central coordination under the purview of the NSRC, a recommendation of the Research
Management Agency (NSRC, 2013). The agency will be accountable for monitoring social requirements, coordinating priority-setting for the areas (especially R&D and STI) recognized by NSRC, administering operation and management of the R&D expenditure and funds, and also assessing the performance of public and private research projects (NSRC,
2013; MOSTI, 2013, 2013b, 2015). The government followed the recommendation and agreed to develop the agency, which would assign and manage all the R&D funds. The agency was officially introduced under the 11th Malaysia Plan (EPU, 2015). Despite serious efforts by the government, stability and flexibility in coordinating STI initiatives and programmes still lack, especially when compared to countries like Singapore and Korea
(NSRC, 2013; Yusoff and Pillai, 2014; Rasiah and Chandran, 2015).
155
5.5 Chapter Summary
The chapter described how the Malaysian economy has experienced remarkable growth and undergone a structural transformation over the decades, from a largely agriculture-based economy to manufacturing and now to a more diversified and knowledge-driven economy.
This significant economic growth was driven by the adoption of specific policies and effective implementation of NIS and strategies that enhanced the growth of the macro- economy and STI-related sectors in line with Vision 2020, as summarized in Tables 5.1 and
5.2. According to the New Economic Model (NEM), STI policies and investment promotion are fundamental to the growth of capital and knowledge-intensive projects, and also to high value-added technology industries (OECD, 2013, 2016).
Since independence in 1957, Malaysia has progressively managed to transform the economy, focusing on STI and high-level R&D activities. Although the government and commentators acknowledge that more needs to be done to build a science-literate society, advanced technology to develop an STI society (EPU, 2015; Rasiah and Chandran, 2015). The 2013
NSRC report stipulated that Malaysia needs more aggressively persist with the STI policies and also their implementation, not only to create scientific knowledge but also to transfer it into reality for breakthrough (or novel) technologies and innovation to enhance the country
TC.
Numerous actions have been taken by the government to overcome limitations and weaknesses in both STI initiatives and overall effectiveness of the programmes. The government started to recognize the importance of STI and industrial policies for TC development at both firm and country-level to attain sustainable economic growth. Since
2013, the Prime Minister has emphasized these objectives with the introduction of wide-
156
ranging programmes such as Science to Action and Science Advisor that aim to streamline and monitor STI initiatives, policies and performance towards continuous development beyond 2020. However, the Vision 2020 now been pushed back as the country recalibrates its economy.
From past experiences, the Malaysia government recognized the challenges and problems that push back the mission to turning the country into an industrialized nation. Therefore, the current Malaysian Prime Minister YAB Tun Dr. Mahathir Mohamad adopted Industrial
Revolution 4.0 (known as National Policy Industry 4.0 - Industry4WRD) in view to accelerated speed in which new technologies evolved. Initially, YAB Tun Dr. Mahathir led the country during a period of rapid growth between 1981 and 2003, and set a target of achieving developed nation status by 2020. Industry4WRD focuses mainly on technological capability developments - digitally transforming Malaysia’s manufacturing sector and its related services to embrace Industry 4.0. The policy envisions Malaysia as a strategic partner for smart manufacturing; focus on high-tech industries by promoting new technology areas such as artificial intelligence and autonomous robots. This initiative to encourage firms to adopt new technologies through collaboration and invest in research and development, with more focus on sectors like electronics, machinery and equipment, aerospace, and medical devices.
Based on above the policies discussion, it clearly can be seen that the Malaysian government put more emphasis on TC building (including new technologies development) through external collaboration as a primary strategy to become an industrialized nation. More recently, the new policy “Industry4WRD” launched in the year 2018 also stressed similar aspects. Malaysia firmly believed that manufacturing sector (firm-level TC development) that
157
has played a crucial role in turning the country into a major player in the global value chain apart from rapidly turning the country into an industrialized nation.
158
CHAPTER 6: QUANTITATIVE FINDINGS
6.1 Introduction
This chapter describes econometric techniques employed to further examine the influence of
IOC on TC building in emerging economies, using firm-level data from 445 manufacturing firms. In Chapter 3, the theoretical framework was developed to link IOC and TC development in emerging economies, inspired by evolutionary theory. In chapter 4, the preliminary methods, data collection, and research variables were discussed in detail. The primary data for quantitative analysis is drawn from the 6th series of the Malaysian National
Survey of Innovation (MNSI), 2009 to 2011. Chapter 5 discussed Malaysia’s innovation policies and the national innovation system (NIS).
The ten hypotheses were tested and the results were presented in this chapter. The statistical software “R” is used in logistic regression (maximum likelihood approach of generalized linear models). In Section 6.2 we summarize the dependent and independent variables in both statistical and graphical form; the main objective of the descriptive analysis is to provide preliminary information about the data collected and determine the characteristics and tendencies of the variables. Section 6.3 justifies the selection of logistic regression, with model specification identifying the regression equations relevant to each hypothesis. In particular, we employ a Logit model, since the dependent variables are binary choice. Section
6.4 discusses the results of the ten hypotheses and alternative models relevant to this study.
159
6.2 Descriptive Statistics of Sample
6.2.1 TC input
TC is measured from two perspectives: an input perspective proxied by the R&D activities or investments that firms do, and an output perspective that is inferred from firm’s innovation outputs such as products or services or processes. Table 6.1 shows the percentage of manufacturing firms engaged in various R&D activities for 2009 to 2011, which we argue implies that they have developed TC. It shows the percentage of firms, which undertook each of the seven R&D activities.
Among the seven R&D activities, internal and external training, and in-house R&D
(intramural) are the highest activities that engaged by manufacturing firms at 68% and 65% respectively. In contrast, the acquisition of R&D (extramural) and acquisition of external knowledge are the lowest R&D activities undertaken during the year 2009 to 2011 at 16% and 21% respectively. Other R&D activities, such as the acquisition of machinery, equipment, and software stand at 56%, the market introduction of innovations stand at 45% and all forms of design stand at 44%. Overall, internal, and external training, and in-house
R&D are the most common activities undertaken by Malaysian firms.
160
Table 6.1: Technological capability input (R&D activities) of Malaysian manufacturing firms during the three year period from 2009 to 2011 (n=445). TC Input Percentage (%) NO YES 1 In-house R&D (intramural R&D) - Creative work undertaken within your company on an occasional or regular basis to increase the stock of 35 65 knowledge and its use to devise new and improved goods, services and processes. 2 TCIN 2: Acquisition of R&D (extramural R&D) - Same activities as above, but purchased by your company and performed by other companies 84 16 (including other companies within your group) or by public or private research organizations. 3 TCIN 3: Acquisition of machinery, equipment and software - Purchase of advanced machinery, equipment and computer hardware or software to 44 56 produce new or significantly improved goods, services, production processes, or delivery methods. 4 TCIN 4: Acquisition of external knowledge - Purchase or licensing of patents and non-patented inventions, know-how and other types of 79 21 knowledge from other companies or organizations. 5 TCIN 5: Training - Internal or external training for your personnel directly 32 68 aimed at the development and/or introduction of innovations. 6 TCIN 6: Market introduction of innovations - Activities for the market preparation and introduction of new or significantly improved goods and 55 45 services, including market research, and launch advertising. 7 TCIN 7: All forms of design - Expenditures on design functions for the development or implementation of new or improved goods, services and 56 44 processes. Expenditure on design in the R&D phase of product development should be excluded.
6.2.1.1 Industry types (TC input)
Figure 6.1 illustrates the low and medium technology (LMT), and high technology firms’ involvement in the seven R&D activities. Based on the descriptive data below, both LMTs and high technology firms are highly engaged in internal and external training, acquisition of machinery, equipment and software, and in-house R&D, at over 50% and 60% respectively.
Fewer than 20% and 25% respectively in both sectors undertook the acquisition of R&D
(extramural) and acquisition of external knowledge. Further, more high technology firms than
LMTs were involved in the market introduction of innovations and all forms of design.
161
100%
90% 17 16 18 24 31 80% 45 46 51 56 70% 59 62 67 72 68 60%
50%
40% 83 84 82 76 69 Yes 30% (%) 55 54 49 No (%) 44 20% 41 38 33 28 32 10%
0% LMTs High LMTs High LMTs High LMTs High LMTs High LMTs High LMTs High tech tech tech tech tech tech tech In-house R&D Acquisition of Acquisition of Acquisition of Internal or Market All forms of R&D machinery, external external introduction design equipment & knowledge training of innovations software Figure 6.1: Compares TC input activities of Low - Medium Technology (n=226) and High Technology firms (n=219) between year 2009 to 2011.
6.2.1.2 Firm size (TC input)
Figure 6.2 compares the involvement of the 258 SMEs and 187 large firms in the seven R&D activities. Based on the descriptive data below, about 258 are small and medium size firms, while 187 are large firms. A large proportion (72%) of large firms are engaged in internal and external training, while around 67% of SMEs undertake in-house R&D. Acquisition of R&D
(extramural) is the activity least frequently used by both SMEs and large firms (16% and
17% respectively).
162
100% 9 90% 18 20 21 31 80% 37 37 47 46 70% 55 60 67 63 68 60% 50% 91 Yes 82 40% 80 79 (%) 69 30% 63 63 53 54 No 20% 45 40 (%) 33 37 32 10% 0% Foreign Firms Foreign Firms Foreign Firms Foreign Firms Foreign Firms Foreign Firms Foreign Firms National Firms National Firms National Firms National Firms National Firms National Firms National Firms In-house R&D Acquisition of Acquisition of Acquisition of Internal or Market All forms of R&D machinery, external external introduction design equipment & knowledge training of innovations software Figure 6.2: Compares TC input activities of Medium-sized enterprises (n=258) and Large firms (n=187) between year 2009 to 2011.
6.2.1.3 Firm’s age or establishment year (TC input)
Figure 6.3 shows that around 197 firms are well-established firms and 248 are new. Some
72% of the established firms are significantly engaged in in-house R&D activities, while 67% of new firms have undertaken internal or external training. In contrast, both groups had the lowest percentages for the acquisition of R&D (16% and 17%) and external knowledge (20% and 22%).
163
100% 9 90% 18 20 21 31 80% 37 37 47 46 70% 55 60 67 63 68 60% 50% 91 Yes 40% 82 80 79 69 (%) 30% 63 63 53 54 No 20% 45 40 33 37 32 (%) 10% 0% Foreign Firms Foreign Firms Foreign Firms Foreign Firms Foreign Firms Foreign Firms Foreign Firms National Firms National Firms National Firms National Firms National Firms National Firms National Firms In-house R&D Acquisition of Acquisition ofAcquisition of Internal or Market All forms of R&D machinery, external external introduction design equipment & knowledge training of software innovations Figure 6.3: Compares TC input activities of the Established firms (year of establishment before 2000) and New firms (year of establishment after 2000) between year 2009 to 2011.
6.2.1.4 Firm ownership (TC input)
Figure 6.4 compares foreign and national firms’ involvement in R&D activities. Around two- thirds of national firms are highly engaged in internal and external training and in-house
R&D, while two-thirds of foreign firms are only involved in internal and external training.
The lowest involvement of both groups is in the acquisition of R&D (9% and 18%) and of external knowledge (20% and 21%).
164
100% 9 90% 18 20 21 31 80% 37 37 47 46 70% 55 60 67 63 68 60% 50% 91 40% 82 80 79 Yes 69 30% 63 63 (%) 53 54 20% 45 40 33 37 32 No (%) 10% 0% Foreign Firms Foreign Firms Foreign Firms Foreign Firms Foreign Firms Foreign Firms Foreign Firms National Firms National Firms National Firms National Firms National Firms National Firms National Firms In-house R&D Acquisition of Acquisition ofAcquisition of Internal or Market All forms of R&D machinery, external external introduction design equipment & knowledge training of software innovations Figure 6.4: Compares TC input activities of Foreign firm (n=380; headquarters located outside Malaysia) and Local firm (n=65; headquarters located in Malaysia) between year 2009 to 2011.
6.2.2 TC Output
Table 6.2 compares the product, service, and process innovation of Malaysian manufacturing firms. The highest percentage of firms engages in new or significantly improved methods of manufacturing (66%), followed by new or significantly improved products (62%). The least involvement is in new or significantly improved logistics, delivery or distribution methods
(36%). Around 53% of firms are involved in new or significantly improved supporting activities and 46% of firms in new or significantly improved services. It is apparent from this table that the majority of manufacturing firms focus on new or significantly improved products and methods of producing goods or services.
165
Table 6.2: Technological capability outputs of Malaysian manufacturing firms during the three year period from 2009 to 2011 (n=445). Technological Capability Output Percentage (%) NO YES 1 TC 1: New or significantly improved products 38 62 2 TC 2: New or significantly improved services 54 46 3 TC 3: New or significantly improved methods of 34 66 manufacturing or producing goods or services 4 TC 4: New or significantly improved logistics, delivery or distribution methods for your inputs, goods 64 36 or services 5 TC 5: New or significantly improved supporting activities for your processes, such as maintenance 47 53 systems or operations for purchasing, accounting, or computing
6.2.2.1 Industry types (TC output)
Figure 6.5 compares the product, service and process innovation outcome of LMTs and high- technology firms. Around 70% of high-technology firms are significantly involved in new or significantly improved methods of manufacturing and new or significantly improved products; the figure for LMTs is 60%. In contrast, only 36% of the firms in both sectors focus on new or significantly improved logistics, delivery or distribution methods. In terms of new or significantly improved services, LMT firms produced more, while new or significantly improved supporting activities are produced more by high-technology firms.
166
100%
90%
80% 36 36 46 45 47 58 70% 59 63 66 69 60%
50% Yes (%) 40% No 30% 64 64 (%) 54 55 53 42 20% 41 37 34 31 10%
0% LMTs High LMTs High LMTs High LMTs High LMTs High tech tech tech tech tech Products Services Methods of Logistics, delivery Supporting manufacturing or distribution activities Figure 6.5: Compares TC output activities of Low - Mediummethods Technology (n=226) and High Technology firms (n=219) between year 2009 to 2011.
6.2.2.2 Firm size (TC output)
Figure 6.6 compares the product, service and process innovation of SMEs and large firms.
The majority of SMEs (73%) focus on new or significantly improved methods of manufacturing; a similar number of large firms (71%) concentrate on new or significantly improved products. For both groups, the lowest involvement was in new or significantly improved logistics, delivery or distribution methods, although the percentage is significantly less in large firms than SMEs.
167
100% 90% 29 80% 41 45 47 48 70% 56 57 58 71 73 60% 50%
40% Yes (%) 71 30% 59 55 53 52 No (%) 20% 44 43 42 29 27 10% 0% SMEs Large SMEs Large SMEs Large SMEs Large SMEs Large Firm Firm Firm Firm Firm Products Services Methods of Logistics, delivery Supporting manufacturing or distribution activities methods
Figure 6.6: Compares TC output activities of Medium-sized enterprises (n=258) and Large firms (n=187) between year 2009 to 2011.
6.2.2.3 Firm’s age (TC output)
Figure 6.7 compares established and new firms’ involvement in five innovation outcome activities. Some two-thirds of the former are highly involved in new or significantly improved methods of manufacturing and new or significantly improved products, while only one-third of the new firms had undertaken these two innovation outcome activities. The data also indicates that the activity with the lowest rate of involvement by both groups is new or significantly improved logistics, delivery or distribution methods, at 34% and 38%.
168
100% 90% 34 80% 43 38 49 52 70% 61 54 64 69 64 60% 50% 40% 66 30% 57 63 51 48 20% 39 46 36 31 36 10% Yes (%) 0% No (%) New Firm New Firm New Firm New Firm New Firm Established Firm Established Firm Established Firm Established Firm Established Firm Products Services Methods of Logistics, delivery Supporting manufacturing or distribution activities methods
Figure 6.7: Compares TC output activities of Established firms (year of establishment before 2000) and New firms (year of establishment after 2000) between year 2009 to 2011.
6.2.2.4 Firm ownership (TC output)
Figure 6.8 compares the five innovation outcomes of foreign and national firms. Both groups are highly involved in new or significantly improved methods of manufacturing and new or significantly improved products, although the percentage is greater for national firms. For
20% of foreign firms the least undertaken outcome is new or significantly improved services, while for national firms the lowest involvement (36%) is in new or significantly improved logistics, delivery or distribution methods.
169
100%
90% 20 80% 35 36 38 50 70% 55 60 55 63 67 60% 50%
40% 80 Yes (%) 30% 65 64 62 50 No (%) 20% 45 40 45 37 33 10%
0% Foreign National Foreign National Foreign National Foreign National Foreign National Firm Firm Firm Firm Firm Firm Firm Firm Firm Firm Products Services Methods of Logistics, delivery Supporting manufacturing or distribution activities methods
Figure 6.8: Compares TC output activities of Foreign firm (n=380; headquarters located outside Malaysia) and Local firm (n=65; headquarters located in Malaysia) between year 2009 to 2011.
6.2.3 IOC with external organizational channels
Table 6.3 illustrates Malaysian manufacturing firms’ IOC for innovation and important of external partners. The data below displays firm’s collaboration activities are with seven types of external organizational partner: suppliers, clients or customers, competitors, consultants, commercial laboratories and private R&D institutes, universities, and government or public research institutes. The importance of IOC with different partners is indicated as low, medium or high.
Malaysian manufacturing firms mainly collaborate with suppliers (43% of the firms), customers (42%), and government research institutes (41%). In contrast, firms co-operate least with consultants (36%), competitors (37%), private R&D institutes (37%) and universities (38%). Around one-third of firms indicate that vertical collaboration with customers (32%) and suppliers (30%) is very important, representing over 70% of the firms that collaborate with these two partners. However, only 14% of the total firms (or around
170
40% of the ones that collaborate with them) considered that collaboration with private R&D institutes and universities is highly important.
Table 6.3: Inter-organizational collaboration for innovation with external organizational channels (importance of co-operating partners) of Malaysian manufacturing firms during the three year period from 2009 to 2011 (n=445). Organizational Partners Percentage (%) Not Relevant Low Medium High Suppliers of equipment, materials, components, 1 57 3 10 30 services or software 2 Clients or customers 58 1 9 32 3 Competitors and other companies in your industry 63 11 9 17 4 Consultants 64 6 13 17 Commercial laboratories and private R&D 5 63 11 12 14 institutes 6 Universities or other higher education institutes 62 11 13 14
7 Government or public research institutes 59 9 14 18
6.2.3.1 Industry types (IOC)
Figure 6.9 compares IOC and the importance of external partners of LMT and high- technology firms. LMTs firms mostly collaborate with suppliers, customers, and competitors, while high technology firms largely co-operate with suppliers, customers and government research institutes. The lowest level of collaboration for LMTS is with consultants and private R&D institutes, and for high-technology with competitors.
Around 36% of high-technology firms (or about 80% of the firms that co-operate) indicated that collaboration with vertical partners; suppliers and customers are is important. Only a quarter of LMT sectors firms (or around 70% of the ones that collaborate) stated co-operation with customers as highly important.
171
100% 10 13 15 13 16 12 90% 22 19 19 24 28 24 36 36 10 15 80% 11 11 13 8 15 8 4 15 70% 12 14 8 11 10 14 9 7 15 4 1 9 10 60% 9 8 10 High (%) 1 2 50% Medium 40% (%) 71 67 62 63 63 65 65 30% 60 58 58 58 Low (%) 54 54 52 20% Not 10% Relevant (%) 0% LMTs High LMTs High LMTs High LMTs High LMTs High LMTs High LMTs High tech tech tech tech tech tech tech Suppliers Customers Competitors Consultants Private R&D Universities Government Figure 6.9: Compares the inter-organizational collaboration activities of LMTs (n=226) and high technology firms (n=219).
6.2.3.2 Firm size (IOC)
Figure 6.10 compares IOC and the important of their external partners of Malaysian SMEs and large firms for the year 2009 to 2011. Manufacturing SMEs co-operate at a high level with suppliers, customers, government research institutes and competitors, and large firms highly co-operate almost with all the external organizations. The lowest level of collaboration for SMEs is with consultants, private R&D institutes and universities, and for large firms is with competitors. Both SMEs and large firms indicated that vertical co-operation with customers and suppliers are greatly important, respectively 28% and 33%. Around one-third of the large firms stated vertical partners highly important, while only 28% of SMEs firms.
172
100% 14 16 13 17 15 14 16 90% 22 22 19 28 28 32 8 80% 37 9 8 11 16 9 9 3 9 8 17 20 High (%) 70% 9 5 9 6 5 2 1 12 13 13 60% 15 19 12 16 Medium 5 50% 1 (%)
40% 73 67 71 70 Low (%) 62 64 66 30% 56 56 50 50 52 51 49 20% Not 10% Relevant (%) 0% SMEs Large SMEs Large SMEs Large SMEs Large SMEs Large SMEs Large SMEs Large Firm Firm Firm Firm Firm Firm Firm Suppliers Customers Competitors Consultants Private R&D Universities Government
Figure 6.10: Compares the inter-organizational collaboration activities of SMEs (n=258) and large firms (n=187).
6.2.3.3 Firm’s Age or establishment year (IOC)
Figure 6.11 compares firm’s IOC and importance of their external partners for established and new firms. Established firms largely collaborate with suppliers and customer, while new firms are mostly co-operate with suppliers, customers and government research institutes. In contrast, established firms co-operate least with private R&D institutes, consultants, competitors and universities, whilst new firms the lowest collaborate only with universities and private R&D institutes. Around 30% of firms in both groups stated that vertical partners are most important.
173
100% 13 90% 16 19 15 18 14 15 16 19 17 31 29 31 32 High (%) 80% 9 10 9 14 10 14 11 10 15 10 17 70% 9 8 8 8 12 7 12 6 12 13 9 Medium 60% 2 1 10 10 4 2 (%) 50% 40% Low (%) 68 68 30% 66 62 64 62 60 55 60 56 60 59 60 56 20% Not Relevant 10% (%) 0% New Firm New Firm New Firm New Firm New Firm New Firm New Firm Established Firm Established Firm Established Firm Established Firm Established Firm Established Firm Established Firm Suppliers Customers Competitors Consultants Private R&D Universities Government Figure 6.11: Compares the inter-organizational collaboration activities of Established firm (year of establishment before 2000, n=197) and New firm (year of establishment after 2000, n=248).
6.2.3.4 Firm ownership (IOC)
Figure 6.12 compares the results for foreign and national firms. Foreign-owned firms notably co-operate with suppliers, competitors and private R&D institute (around 35%), and national firms significantly co-operate with suppliers, customers and government research institutes
(over 40%). Only 6% of foreign firms co-operate with universities and only 15% of while national with private R&D institutes. A quarter of foreign firms stated that vertical partners are highly important, as against a third of national firms.
174
100% 6 12 12 12 12 90% 18 17 15 6 16 19 22 25 31 33 9 80% 12 15 11 12 9 12 18 14 High (%) 6 9 14 70% 6 11 3 8 2 11 7 3 12 11 10 60% 9 10 2 1 Medium 50% (%) 40% 75 Low (%) 68 69 69 65 65 62 63 66 62 30% 56 57 60 57 20% Not Relevant 10% (%) 0% Foreign Firm Foreign Firm Foreign Firm Foreign Firm Foreign Firm Foreign Firm Foreign Firm National Firm National Firm National Firm National Firm National Firm National Firm National Firm Suppliers Customers Competitors Consultants Private R&D Universities Government Figure 6.12: Compares the inter-organizational collaboration activities of foreign firm (headquarters located outside Malaysia, n=65) and national firms (headquarters located in Malaysia, n=380).
6.3 Hypothesis Testing
6.3.1 Logistic Regression
TC was measured from input and output perspectives. The dependent variables (TC input and output) were identified by non-negative integer values because they are counts as the number of scores from a possible list (at least one out of total score). For TC input, for example, if a firm has engaged in at least one of the seven R&D activities listed in Table 6.1, then it will have a TC input coded 1, otherwise 0. For TC output, if the firm has introduced any of the five new or significantly improved products, services or processes listed in Table 6.2, then it is considered that the firm has developed TC and is coded as 1; otherwise 0.
There are several limitations to use linear regression in a research analysis, when the dependent variable is dichotomous (for example, 1 or 0; yes or no). First, the dependent variable “Y” can take only the values 0 and 1, and this restricts on the parameters β
(Wooldridge, 2002; Heij et al., 2004). Furthermore, the linear regression estimation is unable to ensure that the predicted values from the data will remain within 0 and 1 (Wooldridge,
175
2002; Hair et al., 2010). For example, if the estimator's value is less than 0 or greater than 1, then it cannot be considered as an estimation of probabilities.
Secondly, the reality of the binary dependent variables (0 and 1) has properties that violate the assumptions of multiple regression. Where the error terms of the dependent variable are dichotomous, binomial distribution is frequently used rather than normal distribution
(Wooldridge, 2002; Hair et al., 2010). The violation of normality of disturbance estimation is a major cause of the unpredictable parameter estimation, standard errors and significance tests (Allison, 1999; Miles and Shevlin, 2001).
Moreover, if the variance of dichotomous variables is not constant, this suggests the presence of heteroscedasticity (Hair et al., 2010). Ordinary least squares (OLS) become an unbiased procedure by providing the exogenous regressors and leading to the inefficient estimator of β
(Wooldridge, 2002; Heij et al., 2004).
Except in the linear probability model, discriminant analysis can be used, although it depends on strictly achieving the assumptions of multivariate normality and equal variance-covariance matrices across groups (Hair et al., 2010). In any analysis, these propositions are extremely difficult to accomplish.
A Poisson regression model or negative binomial regression is a reasonable option for the case of over-dispersion (Laursen and Salter, 2014). Since, in this study we have two dependent variables that are limited by dichotomous variable (1 is the maximum scores and 0 is the minimum scores), and create Poisson or Negative Binomial distribution or Tobit analysis are not applicable. Instead, we follow Wooldridge (2002: 661), who recommends
176
that the dependent variables could be “obtained from one out of the total score”, which allowed the use of logistic regression or the logit model (Papke and Wooldridge, 1996). In this proposition, E(y|x) is formed as a logit function, where y represents the dependent variable and x represent a set of regressors: E(y|x) = exp(xβ) )/[1 + exp(xβ)].
This model ensures that the estimated values of “y” represent the value of (0 or 1) and the effects of “xј” on E(y|x) diminishes as xβ → ∞. Since this model is non-linear, the pursuing quasi-maximum likelihood can be predicted (Wooldridge, 2002). Furthermore, the models used in this study are apparently unrelated estimations, which allows the researcher to take into account decisions regarding TC input and output. Meanwhile, computing the standard errors, procedure estimates the simultaneous covariance of the coefficients in the two models
(TC input and TC output).
Additionally, the independent variables (IOC-breadth and IOC-depth) are measured in the same way as the dependent variables were measured, taking on non-negative integer values because it’s counts as the number of scores. Again, we followed Wooldridge (2002: 661) who suggests that the independent variable can be “obtained by dividing a count variable by an upper bound”. In spite of applying logistic regression, we normalize the variable by dividing it by the total number of organizational sources (7), so that the outcome of the variables is a minimum value of 0 and a maximum of 1 (Wooldridge, 2002; Laursen and
Salter, 2014).
177
The logistic regression function is identified as:
E(Y) = exp(β0 +β1X1 +...+βnXn)/(1+exp(β0 +β1X1 +...+βnXn)) at 0 ≤ E (Y) ≤ 1
Y = β0 +β1X1 +β2X2 +...... +βnXn +e
Where: E (Y) is the logit function Y is the dependent variable Xi is the independent variable that can be a categorical variable βi is the regression coefficient of Xi β0 is the value of Y in cases where all variables are zero e is the error term.
6.3.2 Model specification
In this section, we identify the model specification of regression equations estimated simultaneously, based on the hypotheses discussed in Chapter 3. Table 6.4 presents all ten hypotheses of this study. Equations for the logit model are then specified.
Table 6.4: Hypotheses of the Research. Code Hypothesis Equation H1 Inter-organizational collaboration breadth has positive 1 relationship/association with technological capability building. H2 Inter-organizational collaboration depth has positive 2 relationship/association with technological capability building. H3 Inter-organizational collaboration depth has a stronger and positive 3 association with technological capability building compare to inter- organizational collaboration breadth. H4 Inter-organizational collaboration depth with suppliers has a positive 4 association with TC building. H5 Inter-organizational collaboration depth with customers has a positive 5 association with TC building. H6 Inter-organizational collaboration depth with competitors has a positive 6 association with TC building. H7 Inter-organizational collaboration depth with consultants has a positive 7 association with TC building. H8 Inter-organizational collaboration depth with private R&D institutes 8 has a positive association with TC building. H9 Inter-organizational collaboration depth with universities has a positive 9 association with TC building. H10 Inter-organizational collaboration depth with government research 10 institutions has a positive association with TC building.
178
Hypothesis 1 Technological Capability = α + β1 (IOC-breadth) + β2 (sub-sector) + β3 (firm size) + β4 (firm age) + β5 (ownership). (1)
Hypothesis 2 Technological Capability = α + β1 (IOC-depth) + β2 (sub-sector) + β3 (firm size) + β4 (firm age) + β5 (ownership). (2)
Hypothesis 3 Technological Capability = α + β1 (IOC-breadth) + β2 (IOC-depth) + β3 (sub- sector) + β3 (firm size) + β5 (firm age) + β6 (ownership). (3)
Hypothesis 4 Technological Capability = α + β1 (IOC-depth suppliers) + β2 (sub-sector) + β3 (firm size) + β4 (firm age) + β5 (ownership). (4)
Hypothesis 5 Technological Capability = α + β1 (IOC-depth customers) + β2 (sub-sector) + β3 (firm size) + β4 (firm age) + β5 (ownership). (5)
Hypothesis 6 Technological Capability = α + β1 (IOC-depth competitors) + β2 (sub-sector) + β3 (firm size) + β4 (firm age) + β5 (ownership). (6)
Hypothesis 7 Technological Capability = α + β1 (IOC-depth consultants) + β2 (sub-sector) + β3 (firm size) + β4 (firm age) + β5 (ownership). (7)
Hypothesis 8 Technological Capability = α + β1 (IOC-depth private R&D) + β2 (sub-sector) + β3 (firm size) + β4 (firm age) + β5 (ownership). (8)
Hypothesis 9 Technological Capability = α + β1 (IOC-depth universities) + β2 (sub-sector) + β3 (firm size) + β4 (firm age) + β5 (ownership). (9)
Hypothesis 10 Technological Capability = α + β1 (IOC-depth government research institutions) + β2 (sub-sector) + β3 (firm size) + β4 (firm age) + β5 (10) (ownership).
179
6.4 Results of Hypothesis Testing
6.4.1 IOC-breadth and IOC-depth, and TC building (hypotheses 1-3).
The main results of the logistic regression are presented in Tables 6.5 and 6.6. The dependent variables of the Models 1 to 4 in Table 6.5 represent TC output and Table 6.6 represent TC input, with standard error in brackets. The estimations in both Tables in Model (1) includes only the control variables.34 In Model (2) IOC-breadth (independent variable) and Model (3)
IOC-depth (independent variable) are entered in the base model, and Model (4) includes both
(IOC-breadth and IOC-depth)
Of the four control variables, Table 6.5 Model (1) shows that only sub-sectors and firm ownership are statistically significant at the 1% level (with p<0.01, coefficient = 1.367, standard error = 0.366) and 5% level (p<0.05, coefficient = 1.027, standard error = 0.425) respectively. In contrast, neither firm size nor year of establishment (age) is statistically significant. For goodness of fit in Model (1), AIC = 287.002 and BIC = 307.493.35
On the other hand, in Table 6.6 Model (1) only sub-sectors and year of establishment are statistically significant at the 1% level (with p<0.01, coefficient = 0.611, standard error =
0.215) and 10% level (with p<0.10, coefficient = -0.397, standard error = 0.231) respectively.
Firm size and ownership are not statistically significant. For goodness of fit, in Model (1)
AIC = 531.067 and BIC = 551.558.
34 The estimate of all the models using the maximum likelihood approach of generalized linear models (the command glm is used in R). 35 Akaike Information Criteria (AIC) and Bayesian Information Criterioa (BIC) are important indicators of a model’s goodness of fit. They are penalized by increased values of coefficients in the model. In other words, adding more variables to the same model will not increase AIC and BIC values. Avoiding over-fitting. AIC or BIC metrics is more helpful than some logistic regression models in identifying which model has the better fit. Looking at the AIC or BIC metric of one model does not add any advantage. The model with the lowest AIC and BIC will be the relatively better model. 180
6.4.1.1 Hypothesis 1
Hypothesis 1 predicted that IOC-breadth is positively associated with TC building of
Malaysian manufacturing firms. This is supported by the logistic regression results of Model
(2) in Table 6.5 (dependent variable TC output) and Table 6.6 (dependent variable TC input).
The results in Table 6.5 show that IOC-breadth is statistically significant with TC building
(TC output) at the 1% level (with p<0.01, coefficient = 1.352, standard error = 0.442). Firms that form IOC-breadth are more likely to develop TC than those not involved in IOC-breadth.
In other words, for every one unit change in IOC-breadth, the log odds of TC building
(versus not developed TC) increase by 1.352. Of the four control variables, only sub-sectors and firm ownership are statistically significant for hypothesis 1 at the 1% level (with p<0.01, coefficient = 1.325, standard error = 0.373) and 5% level (p<0.05, coefficient = 0.933, standard error = 0.438) respectively. The coefficient on sub-sectors is positive and significant showing that firms from high-technology firms are more likely to change the log odds of TC building by 1.325, relative to LMTs (high-tech = 1 and LMTs = 0). Similarly, firm ownership is positively significant, reflecting that national firms are more likely to change the log odds of TC building by 0.933 compared to foreign firms (national firm = 1 and foreign firm = 0).
The other two control variables, firm size and year of establishment, are not statistically significant.
The analysis in Table 6.6 shows that IOC-breadth is statistically significant with TC building
(TC input) at the 1% level (with p<0.01, coefficient = 1.386, standard error = 0.274). This reveals that firms engaged in IOC-breadth are likely to increase the log odds of TC development by 1.386 compared to those not involved in IOC-breadth. The control variables sub-sectors and year of establishment are statistically significant for hypothesis 1 at the 1%
181
level (with p<0.01, coefficient = 0.532, standard error = 0.223) and 10% level (with p<0.05, coefficient = -0.481, standard error = 0.240) respectively. Sub-sectors is positively significant, indicating that high-tech firms are likely to change the log odds of TC building by 0.532, compared to LMTs, while the year of establishment is negatively significant, showing the likelihood of established firms changes the log odds of TC building by 0.481 relative to new firms (new firm = 1 and established firm = 0). The other two control variables, firm size and ownership, are not statistically significant.
6.4.1.2 Hypothesis 2
Hypothesis 2 proposes that there is a positive relationship between IOC-depth and TC building. The results of Model (3) in Table 6.5 (dependent variable TC output) and Table 6.6
(dependent variable TC input) support the prediction of hypothesis 2.
The results of logit models in Table 6.6 reveal that IOC-depth is statistically significant with
TC development (TC output) at the 1% level (with p<0.01, coefficient = 4.862, standard error
= 1.569). This indicates that for every one unit change in IOC-depth, the log odds of TC building (versus not developed TC) increased by 4.862. Sub-sectors and firm ownership are statistically significant with hypothesis 2 at the 1% level (with p<0.01, coefficient = 1.269, standard error = 0.374) and 5% level (p<0.05, coefficient = 0.886, standard error = 0.441) respectively. Sub-sectors is positively significant, reflecting that high-tech firms are highly likely to change the log odds of TC building by 1.269 compared with LMTs, while the coefficient of ownership shows that national firms are likely to change the log odds of TC building by 0.886 in relation to foreign firms. Neither firm size nor year of establishment is statistically significant.
182
The results in Table 6.6 indicate that IOC-depth is statistically significant with TC development (TC input) at the 5% level (with p<0.05, coefficient = 0.815, standard error =
0.393). This outcome suggests that firms engaged in IOC-depth are more likely to increase the log odds of TC development by 1.386 compared to those firms not involved in IOC-depth.
Only sub-sectors and year of establishment are statistically significant at the 5% level (with p<0.05, coefficient = 0.549, standard error = 0.218) and 10% level (with p<0.10, coefficient
= -0.398, standard error = 0.232) respectively. The coefficient of sub-sectors is positively significant, indicating that high-tech firms could increase the log odds of TC building by
0.549 in relation to LMTs. Year of establishment is negatively significant, showing that the likelihood of established firms changing the log odds of TC building is by 0.398 relative to new firms. Neither firm size nor ownership is statistically significant.
6.4.1.3 Hypothesis 3
Hypothesis 3 predicted that IOC-depth has a stronger, positive association with TC building than IOC-breadth. The results of Model (4) in Table 6.5 and Table 6.6 partially support the hypothesis 3.
In the case of TC building (TC output), of the two independent variables only IOC-depth is statistically significant at the 5% level (with p<0.05, coefficient = 5.269, standard error =
2.088); IOC-breadth is not statistically significant. This strongly supports the prediction of hypothesis 3, since IOC-depth persistence effect is stronger and more significant, while IOC- breadth is not significant. Sub-sectors is strongly significant with the hypothesis at 1%, while firm ownership is statistically significant at 5%.
183
On the other hand, for TC building (TC input), only IOC-breadth is positively significant
(coefficient = 2.636, standard error = 0.532); IOC-depth is negatively significant (coefficient
= -2.186, standard error = 0.701). Both variables are statistically significant at the 5% level.
Therefore, the results indicate that the effect of IOC-breadth is stronger and more significant than IOC-depth in relation to TC development (TC input). This result does not support the prediction of hypothesis 3. Further, sub-sectors is positively significant at the 1% level, while year of establishment and firm size are negatively significant at 5% and 10% levels respectively.
Table 6.5: Logistic regression analysis (weighted results) of TC-output and IOC (breadth and depth). (1) (2) (3) (4) (Intercept) 0.917 (0.535)* 0.712 (0.552) 0.648 (0.554) 0.649 (0.554) Sub-Sectors 1.367 (0.366)*** 1.325 (0.373)*** 1.269 (0.374)*** 1.268 (0.374)*** Size 0.186 (0.356) 0.080 (0.368) 0.191 (0.378) 0.211 (0.383) Year - 0.313 (0.361) - 0.378 (0.366) - 0.328 (0.371) - 0.319 (0.372) Ownership 1.027 (0.425)** 0.933 (0.438)** 0.886 (0.441)** 0.890 (0.441)** IOC-Breadth 1.352 (0.442)*** - 0.184 (0.589) IOC-Depth 4.862 (1.569)*** 5.269 (2.088)** AIC 287.002 277.717 267.478 269.382 BIC 307.493 302.306 292.066 298.068 Log Likelihood - 138.501 - 132.859 - 127.739 - 127.691 Deviance 277.002 265.717 255.478 255.382 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.6: Logistic regression analysis (weighted results) of TC-input and IOC (breadth and depth). (1) (2) (3) (4) (Intercept) 0.717 (0.374)* 0.611 (0.390) 0.664 (0.378)* 0.618 (0.396) Sub-Sectors 0.611 (0.215)*** 0.532 (0.223)** 0.549 (0.218)** 0.620 (0.230)*** Size - 0.097 (0.225) - 0.313 (0.240) - 0.119 (0.229) - 0.397 (0.242)* Year - 0.397 (0.231)* - 0.481 (0.240)** - 0.398 (0.232)* - 0.532 (0.243)** Ownership 0.213 (0.309) 0.001 (0.324) 0.143 (0.313) 0.029 (0.328) IOC-Breadth 1.386 (0.274)*** 2.636 (0.532)*** IOC-Depth 0.815 (0.393)** - 2.186 (0.701)*** AIC 531.067 504.285 528.440 495.762 BIC 551.558 528.873 553.029 524.449 Log Likelihood - 260.534 - 246.142 - 258.220 - 240.881 Deviance 521.067 492.285 516.440 481.762 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
184
6.4.2 Impact of different partners: IOC-dDepth and TC (hypotheses 4-10).
The results of the logistic regression (hypotheses 4 to 10) are presented in Tables 6.7 and 6.8.
The dependent variables in Models 1 to 8 represent TC output in Table 6.7, and TC input in
Table 6.8, with standard error in brackets. The first model in both Tables 6.7 and 6.8 includes only the control variables. Models 2 to 8 include independent variables (IOC-depth): Model 2 represents suppliers, followed by Model 3 customers, Model 4 competitors, Model 5 consultants, Model 6 private R&D institutes, Model 7 universities, and Model 8 government research institutions.
From the four control variables, Table 6.7 Model (1) shows that only sub-sectors and firm ownership are statistically significant at the 1% level (with p<0.01, coefficient = 1.367, standard error = 0.366) and 5% level (p<0.05, coefficient = 1.027, standard error = 0.425) respectively. Neither firm size nor year of establishment is statistically significant. For goodness of fit, Model (1), AIC = 287.002 and BIC = 307.493.
In Table 6.8 Model (1) only sub-sectors and year of establishment are statistically significant at the 1% level (with p<0.01, coefficient = 0.611, standard error = 0.215) and 10% level (with p<0.10, coefficient = -0.397, standard error = 0.231) respectively. Neither firm size nor ownership is statistically significant. Model (1)’s goodness of fit is AIC = 531.067 and BIC =
551.558.
6.4.2.1 Hypothesis 4
Hypothesis 4 proposes that IOC-depth, suppliers has a positive relationship with TC building.
This prediction is strongly supported by the results of Model (2) in Table 6.7 (dependent variable TC output) and Table 6.8 (dependent variable TC input).
185
The results in Table 6.7 show that IOC-depth, suppliers is statistically significant at the 1% level (with p<0.01, coefficient = 2.226, standard error = 0.735). Only two control variables, sub-sectors and firm ownership, are statistically significant at the 1% level (with p<0.01, coefficient = 1.244, standard error = 0.371) and 5% level (p<0.05, coefficient = 0.927, standard error = 0.439) respectively. Firm size and year of establishment are not statistically significant.
The analysis in Table 6.8 shows that IOC-depth, suppliers is statistically significant at the 5% level (with p<0.05, coefficient = 0.489, standard error = 0.250). This is consistent with control variables sub-sectors and year of establishment, which are statistically significant at the 1% level (with p<0.01, coefficient = 0.561, standard error = 0.217) and 10% level (with p<0.10, coefficient = -0.394, standard error = 0.232) respectively. On the other hand, the other two control variables, firm size and ownership, are not statistically significant.
6.4.2.2 Hypothesis 5
Hypothesis 5 proposes that there is a significant positive association of IOC-depth, customers and TC building. The prediction is strongly supported by the results of Model (3) in Tables
6.7 and 6.8.
The results of the logit models in Table 6.7 reveal that IOC-depth, customers are statistically significant at the 1% level (with p<0.01, coefficient = 2.432, standard error = 0.735). Only sub-sectors and firm ownership are statistically significant at the 1% level (with p<0.01, coefficient = 1.337, standard error = 0.375) and 5% level (p<0.05, coefficient = 0.980,
186
standard error = 0.444) respectively. Neither firm size nor year of establishment is statistically significant.
The results in Table 6.8 indicate that IOC-depth, customers is statistically significant at the
1% level (with p<0.01, coefficient = 0.915, standard error = 0.259). Only sub-sectors and year of establishment are statistically significant at the 1% level (with p<0.01, coefficient =
0.564, standard error = 0.219) and 10% level (with p<0.10, coefficient = -0.410, standard error = 0.234) respectively. Neither firm size nor ownership is statistically significant.
6.4.2.3 Hypothesis 6
Hypothesis 6 predicted that IOC–depth, competitors has a positive association with TC building. This is partially supported by the results in Tables 6.7 and 6.8: TC output is supported but TC input is not.
The results in Table 6.7 show that the IOC depth, competitors link is statistically significant at the 10% significance level (with p<0.10, coefficient = 1.399, standard error = 0.742). Only two control variables, sub-sectors and ownership, are statistically significant at the 1% level
(with p<0.01, coefficient = 1.260, standard error = 0.367) and 5% level (p<0.05, coefficient =
0.929, standard error = 0.436) respectively. Firm size and year of establishment are not statistically significant.
The results in Table 6.8 show that IOC–depth, competitors is not statistically significant
(coefficient = 0.103, standard error = 0.291). However, sub-sectors and year of establishment are statistically significant at the 1% level (with p<0.01, coefficient = 0.601, standard error =
187
0.217) and 10% level (with p<0.10, coefficient = -0.399, standard error = 0.231) respectively.
Firm size and ownership are not statistically significant.
6.4.2.4 Hypothesis 7
Hypothesis 7 shows that IOC-depth, consultants has a positive association with TC building.
The results of Model (5) in Tables 6.7 and 6.8 partially support this; TC output supports hypothesis 7, but TC input does not.
The results in Table 6.7 indicate that IOC-depth, consultants isstatistically significant at the
5% level (with p<0.05, coefficient = 1.505, standard error = 0.742). Only sub-sectors and firm ownership are statistically significant at the 1% level (with p<0.01, coefficient = 1.335, standard error = 0.369) and 5% level (p<0.05, coefficient = 0.984, standard error = 0.429) respectively. Neither firm size nor year of establishment is statistically significant.
Table 6.8 shows that IOC-depth, consultants are not statistically significant (coefficient =
0.416, standard error = 0.308). However, sub-sectors and year of establishment are statistically significant at the 1% level (with p<0.01, coefficient = 0.594, standard error =
0.217) and 10% level (with p<0.10, coefficient = -0.410, standard error = 0.232) respectively.
The two other control variables, firm size and ownership, are not statistically significant.
6.4.2.5 Hypothesis 8
Hypothesis 8 predicted that IOC-depth, private R&D institutes has a positive association with
TC building. However, the results of Model (8) in Tables 6.7 and 6.8 do not support this, and based on the results from TC output and input, hypothesis 8 is rejected.
188
The results in Table 6.7 show that IOC depth, private R&D institutes is not statistically significant (coefficient = 16.298, standard error = 784.883), although sub-sectors and ownership are statistically significant at the 1% level (with p<0.01, coefficient = 1.227, standard error = 0.368) and 5% level (p<0.05, coefficient = 1.009, standard error = 0.439) respectively. Firm size and year of establishment are not statistically significant.
Table 6.8 also shows that IOC depth, private R&D institutes is not statistically significant
(coefficient = 0.561, standard error = 0.343). However, sub-sectors and year of establishment are statistically significant at the 1% level (with p<0.01, coefficient = 0.561, standard error =
0.217) and 10% level (with p<0.10, coefficient = -0.396, standard error = 0.232) respectively.
Firm size and ownership are not statistically significant.
6.4.2.6 Hypothesis 9
Hypothesis 9 proposed that IOC-depth, universities have a positive association with TC building. This prediction is partially supported by the results of Model (7) in Tables 6.7 and
6.8: TC input supports the hypothesis, but TC output does not.
The results of logit models in Table 6.7 reveal that IOC-depth, universities is not statistically significant (coefficient = 16.357, standard error = 783.188). However, sub-sectors and firm ownership are statistically significant at the 1% level (with p<0.01, coefficient = 1.316, standard error = 0.368) and 5% level (p<0.05, coefficient = 0.939, standard error = 0.431) respectively. Firm size and year of establishment are not statistically significant.
However, the results in Table 6.8 indicate that IOC depth, universities are statistically significant at the 10% level (with p<0.10, coefficient = 0.577, standard error = 0.343). This is
189
consistent with sub-sectors and year of establishment, which are statistically significant at the
1% level (with p<0.01, coefficient = 0.588, standard error = 0.216) and 10% level (with p<0.10, coefficient = -0.387, standard error = 0.232) respectively, although firm size and ownership are not statistically significant.
6.4.2.7 Hypothesis 10
Hypothesis 10 predicted that IOC-depth, government research institutions has a positive association with TC building. The results of Model (8) in Tables 6.7 and Table 6.8 partially support this hypothesis.
The results of logit models in Table 6.7 show that IOC-depth, government research institutions is statistically significant at the 10% level (with p<0.10, coefficient = 1.367, standard error = 0.745). Only sub-sectors and firm ownership are statistically significant at the 1% level (with p<0.01, coefficient = 1.238, standard error = 0.369) and 5% level (p<0.05, coefficient = 1.003, standard error = 0.431) respectively. Neither firm size nor year of establishment is statistically significant.
However, the results presented in Table 6.8 indicate that IOC-depth, government research institutions is not statistically significant (coefficient = 0.033, standard error = 0.292).
Nevertheless, sub-sectors and year of establishment are statistically significant at the 1% level (with p<0.01, coefficient = 0.607, standard error = 0.219) and 10% level (with p<0.10, coefficient = -0.396, standard error = 0.231) respectively. The other two control variables, firm size and ownership, are not statistically significant.
190
Table 6.7: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. (1) (2) (3) (4) (5) (6) (7) (8) (Intercept) 0.917 (0.535)* 0.718 (0.548) 0.648 (0.556)* 0.915 (0.544)* 0.813 (0.539) 0.780 (0.550) 0.783 (0.543) 0.799 (0.543) Sub-Sectors 1.367 (0.366)*** 1.244 (0.371)*** 1.337 (0.375)*** 1.260 (0.367)*** 1.335 (0.369)*** 1.227 (0.368)*** 1.316 (0.368)*** 1.238 (0.369)*** Size - 0.186 (0.356) 0.193 (0.372) 0.111 (0.373) 0.161 (0.368) 0.239 (0.363) 0.252 (0.364) 0.251 (0.367) 0.262 (0.366) Year - 0.313 (0.361) - 0.317 (0.369) - 0.344 (0.367) - 0.333 (0.362) - 0.343 (0.366) - 0.293 (0.363) - 0.264 (0.366) - 0.287 (0.362) Ownership 1.027 (0.425)** 0.927 (0.439)** 0.980 (0.444)** 0.929 (0.436)** 0.984 (0.429)** 1.009 (0.439)** 0.939 (0.431)** 1.003 (0.431)** Suppliers 2.226 (0.735) *** Competitors 2.432 (0.735)*** 1.399 (0.742)* Consultants 1.505 (0.742)** Private R&D 16.298 (784.883) Universities 16.357 (783.188) Government 1.367 (0.745) * AIC 287.002 271.772 267.340 283.787 282.778 276.778 276.073 284.161 BIC 307.493 296.361 291.929 308.376 307.377 301.367 300.661 308.750 Log Likelihood - 138.502 - 129.886 - 127.670 - 135.894 - 135.389 - 132.389 - 132.036 - 132.081 Deviance 277.002 259.772 271.340 271.787 270.778 264.778 264.073 272.161 Number Obs. 445 445 445 445 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.8: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. (1) (2) (3) (4) (5) (6) (7) (8) (Intercept) 0.717 (0.374)* 0.659 (0.377)* 0.613 (0.381)* 0.718 (0.375)* 0.692 (0.376)* 0.692 (0.377)* 0.690 (0.376)* 0.714 (0.375)* Sub-Sectors 0.611 (0.215)*** 0.561 (0.217)*** 0.564 (0.219)*** 0.601 (0.217)*** 0.594 (0.216)*** 0.561 (0.217)*** 0.588 (0.216)*** 0.607 (0.219)*** Size - 0.097 (0.225) - 0.116 (0.228) - 0.180 (0.232) - 0.105 (0.227) - 0.096 (0.227) - 0.106 (0.228) - 0.096 (0.227) - 0.096 (0.226) Year - 0.397 (0.231)* - 0.394 (0.232)* - 0.410 (0.234)* - 0.399 (0.232)* - 0.410 (0.232)* - 0.396 (0.232)* - 0.387 (0.232)* - 0.396 (0.231)* Ownership 0.213 (0.309) 0.158 (0.312) 0.118 (0.315) 0.201 (0.310) 0.185 (0.310) 0.188 (0.311) 0.162 (0.311) 0.211 (0.309) Suppliers 0.489 (0.250)** Customers 0.915 (0.259)*** Competitors 0.103 (0.291) Consultants 0.416 (0.308) Private R&D 0.561 (0.343) Universities 0.577 (0.343) * Government 0.033 (0.292) AIC 531.067 529.068 519.403 532.941 531.148 530.148 529.989 533.054 BIC 551.558 553.657 543.992 557.529 555.736 554.756 554.577 557.643 Log Likelihood - 260.534 - 258.534 - 253.702 - 260.470 - 259.574 - 259.084 - 258.994 - 260.527 Deviance 521.067 517.068 507.403 520.941 519.148 518.168 517.989 521.054 Number Obs. 445 445 445 445 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
191
6.5 Robustness Tests and Alternative Explanations
To test the robustness of the research findings to this potential problem, we make estimates from three different models. First, we estimate the models using an alternative measure of our dependent variable for both TC input and TC output (Section 6.5.1). The new TC input variable is considered if a firm has undertaken at least two of the nine potential R&D activities. Similarly, the new TC output variable is considered if a firm has introduced at least two of the five potential innovation outcomes. (new product, service or processes) from potential five firms outcome of innovation. Secondly, we estimate the models using an alternative measure for the independent variable (Section 6.5.2). Therefore, we were using the source of information for innovation for IOC partners (for example, see
Belderbos et al., 2004, 2004b; Veugelers and Cassiman, 2005). Thirdly, we used a probit model as an alternative to the logit model, following the example of Laursen and Salter
(2014); used a similar method for robustness test) see Section 6.5.3.
6.5.1 Alternative to dependent variables
The results for the alternative measure for dependent variable TC output are shown in
Tables 6.9, 6.11 and 6.13. The overall results are very similar to those from the main logistic regression analysis. The findings for TC input are also very similar to the main research findings, as shown in Tables 6.10, 6.12 and 6.14. Both sets of results indicate that the dependent variable is robust.
192
Table 6.9: Logistic regression analysis (weighted results) of TC-output and IOC (breadth and depth). (1) (2) (3) (4) (Intercept) 0.917 (0.535)* 0.712 (0.552) 0.648 (0.554) 0.649 (0.554) Sub-Sectors 1.367 (0.366)*** 1.325 (0.373)*** 1.269 (0.374)*** 1.268 (0.374)*** Size 0.186 (0.356) 0.080 (0.368) 0.191 (0.378) 0.211 (0.383) Year - 0.313 (0.361) - 0.378 (0.366) - 0.328 (0.371) - 0.319 (0.372) Ownership 1.027 (0.425)** 0.933 (0.438)** 0.886 (0.441)** 0.890 (0.441)** IOC-Breadth 1.352 (0.442)*** - 0.184 (0.589) IOC-Depth 4.862 (1.569)*** 5.269 (2.088)** AIC 287.002 277.717 267.478 269.382 BIC 307.493 302.306 292.066 298.068 Log Likelihood - 138.501 - 132.859 - 127.739 - 127.691 Deviance 277.002 265.717 255.478 255.382 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.10: Logistic regression analysis (weighted results) of TC-input and IOC (breadth and depth). (1) (2) (3) (4) (Intercept) 0.717 (0.374)* 0.611 (0.390) 0.664 (0.378)* 0.618 (0.396) Sub-Sectors 0.611 (0.215)*** 0.532 (0.223)** 0.549 (0.218)** 0.620 (0.230)*** Size - 0.097 (0.225) - 0.313 (0.240) - 0.119 (0.229) - 0.397 (0.242)* Year - 0.397 (0.231)* - 0.481 (0.240)** - 0.398 (0.232)* - 0.532 (0.243)** Ownership 0.213 (0.309) 0.001 (0.324) 0.143 (0.313) 0.029 (0.328) IOC-Breadth 1.386 (0.274)*** 2.636 (0.532)*** IOC-Depth 0.815 (0.393)** - 2.186 (0.701)*** AIC 531.067 504.285 528.440 495.762 BIC 551.558 528.873 553.029 524.449 Log Likelihood - 260.534 - 246.142 - 258.220 - 240.881 Deviance 521.067 492.285 516.440 481.762 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
193
Table 6.11: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. (1) (2) (3) (4) (5) (6) (7) (8) (Intercept) 0.917 (0.535)* 0.718 (0.548) 0.648 (0.556)* 0.915 (0.544)* 0.813 (0.539) 0.780 (0.550) 0.783 (0.543) 0.799 (0.543) Sub-Sectors 1.367 (0.366)*** 1.244 (0.371)*** 1.337 (0.375)*** 1.260 (0.367)*** 1.335 (0.369)*** 1.227 (0.368)*** 1.316 (0.368)*** 1.238 (0.369)*** Size - 0.186 (0.356) 0.193 (0.372) 0.111 (0.373) 0.161 (0.368) 0.239 (0.363) 0.252 (0.364) 0.251 (0.367) 0.262 (0.366) Year - 0.313 (0.361) - 0.317 (0.369) - 0.344 (0.367) - 0.333 (0.362) - 0.343 (0.366) - 0.293 (0.363) - 0.264 (0.366) - 0.287 (0.362) Ownership 1.027 (0.425)** 0.927 (0.439)** 0.980 (0.444)** 0.929 (0.436)** 0.984 (0.429)** 1.009 (0.439)** 0.939 (0.431)** 1.003 (0.431)** Suppliers 2.226 (0.735) *** Competitors 2.432 (0.735)*** 1.399 (0.742)* Consultants 1.505 (0.742)** Private R&D 16.298 (784.883) Universities 16.357 (783.188) Government 1.367 (0.745) * AIC 287.002 271.772 267.340 283.787 282.778 276.778 276.073 284.161 BIC 307.493 296.361 291.929 308.376 307.377 301.367 300.661 308.750 Log Likelihood - 138.502 - 129.886 - 127.670 - 135.894 - 135.389 - 132.389 - 132.036 - 132.081 Deviance 277.002 259.772 271.340 271.787 270.778 264.778 264.073 272.161 Number Obs. 445 445 445 445 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.12: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. (1) (2) (3) (4) (5) (6) (7) (8) (Intercept) 0.717 (0.374)* 0.659 (0.377)* 0.613 (0.381)* 0.718 (0.375)* 0.692 (0.376)* 0.692 (0.377)* 0.690 (0.376)* 0.714 (0.375)* Sub-Sectors 0.611 (0.215)*** 0.561 (0.217)*** 0.564 (0.219)*** 0.601 (0.217)*** 0.594 (0.216)*** 0.561 (0.217)*** 0.588 (0.216)*** 0.607 (0.219)*** Size - 0.097 (0.225) - 0.116 (0.228) - 0.180 (0.232) - 0.105 (0.227) - 0.096 (0.227) - 0.106 (0.228) - 0.096 (0.227) - 0.096 (0.226) Year - 0.397 (0.231)* - 0.394 (0.232)* - 0.410 (0.234)* - 0.399 (0.232)* - 0.410 (0.232)* - 0.396 (0.232)* - 0.387 (0.232)* - 0.396 (0.231)* Ownership 0.213 (0.309) 0.158 (0.312) 0.118 (0.315) 0.201 (0.310) 0.185 (0.310) 0.188 (0.311) 0.162 (0.311) 0.211 (0.309) Suppliers 0.489 (0.250)** Customers 0.915 (0.259)*** Competitors 0.103 (0.291) Consultants 0.416 (0.308) Private R&D 0.561 (0.343) Universities 0.577 (0.343) * Government 0.033 (0.292) AIC 531.067 529.068 519.403 532.941 531.148 530.148 529.989 533.054 BIC 551.558 553.657 543.992 557.529 555.736 554.756 554.577 557.643 Log Likelihood - 260.534 - 258.534 - 253.702 - 260.470 - 259.574 - 259.084 - 258.994 - 260.527 Deviance 521.067 517.068 507.403 520.941 519.148 518.168 517.989 521.054 Number Obs. 445 445 445 445 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
194
Table 6.13: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. (1) (2) (3) (4) (Intercept) 0.917 (0.535)* 0.600 (0.556) 0.915(0.544)* 0.658 (0.550) Sub-Sectors 1.367 (0.366)*** 1.309 (0.376)*** 1.260 (0.367)*** 1.277 (0.370)*** Size 0.186 (0.356) 0.123 (0.374) 0.161 (0.368) 0.351 (0.375) Year - 0.313 (0.361) - 0.330 (0.369) - 0.333 (0.362) - 0.318 (0.369) Ownership 1.027 (0.425)** 0.986 (0.443)** 0.929 (0.436)** 0.945 (0.437)** Vertical Collaboration 2.899 (0.873)*** Horizontal Collaboration 1.399 (0.742)* Institution Collaboration (Knowledge Provider) 4.501 (1.771)** AIC 287.002 265.926 283.787 274.346 BIC 307.493 290.515 308.376 298.934 Log Likelihood - 138.501 - 126.963 - 135.894 - 131.173 Deviance 277.002 253.926 271.787 262.346 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.14: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. (1) (2) (3) (4) (Intercept) 0.717 (0.374)* 0.624 (0.379)* 0.718(0.375)* 0.681 (0.377)* Sub-Sectors 0.611 (0.215)*** 0.552 (0.218)** 0.601 (0.217)*** 0.568 (0.217)*** Size - 0.097 (0.225) - 0.150 (0.230) - 0.105 (0.227) - 0.092 (0.227) Year - 0.397 (0.231)* - 0.400 (0.313)* - 0.399 (0.231)* - 0.396 (0.232)** Ownership 0.213 (0.309) 0.130 (0.271) 0.201 (0.310) 0.176 (0.311) Vertical Collaboration 0.789 (0.271)*** Horizontal Collaboration 0.103 (0.291) Institution Collaboration (Knowledge Provider) 0.576 (0.398) AIC 531.067 523.942 523.941 530.828 BIC 551.558 548.530 557.529 555.416 Log Likelihood - 260.534 - 255.971 - 260.470 - 259.414 Deviance 521.067 511.942 520.941 518.828 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
195
6.5.2 Alternative to independent variables
The results for the alternative measures of IOC are shown in Tables 6.15 to 6.20. The overall of findings are very similar to the main research results, indicating that the independent variables are robust.
Table 6.15: Logistic regression analysis (weighted results) of TC-output and IOC (breadth and depth). (1) (2) (3) (4) (Intercept) 0.917 (0.535)* 0.712 (0.552) 0.648 (0.554) 0.649 (0.554) Sub-Sectors 1.367 (0.366)*** 1.325 (0.373)*** 1.269 (0.374)*** 1.268 (0.374)*** Size 0.186 (0.356) 0.080 (0.368) 0.191 (0.378) 0.211 (0.383) Year - 0.313 (0.361) - 0.378 (0.366) - 0.328 (0.371) - 0.319 (0.372) Ownership 1.027 (0.425)** 0.933 (0.438)** 0.886 (0.441)** 0.890 (0.441)** IOC-Breadth 1.352 (0.442)*** - 0.184 (0.589) IOC-Depth 4.862 (1.569)*** 5.269 (2.088)** AIC 287.002 277.717 267.478 269.382 BIC 307.493 302.306 292.066 298.068 Log Likelihood - 138.501 - 132.859 - 127.739 - 127.691 Deviance 277.002 265.717 255.478 255.382 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.16: Logistic regression analysis (weighted results) of TC-input and IOC (breadth and depth). (1) (2) (3) (4) (Intercept) 0.717 (0.374)* 0.611 (0.390) 0.664 (0.378)* 0.618 (0.396) Sub-Sectors 0.611 (0.215)*** 0.532 (0.223)** 0.549 (0.218)** 0.620 (0.230)*** Size - 0.097 (0.225) - 0.313 (0.240) - 0.119 (0.229) - 0.397 (0.242)* Year - 0.397 (0.231)* - 0.481 (0.240)** - 0.398 (0.232)* - 0.532 (0.243)** Ownership 0.213 (0.309) 0.001 (0.324) 0.143 (0.313) 0.029 (0.328) IOC-Breadth 1.386 (0.274)*** 2.636 (0.532)*** IOC-Depth 0.815 (0.393)** - 2.186 (0.701)*** AIC 531.067 504.285 528.440 495.762 BIC 551.558 528.873 553.029 524.449 Log Likelihood - 260.534 - 246.142 - 258.220 - 240.881 Deviance 521.067 492.285 516.440 481.762 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
196
Table 6.17: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. (1) (2) (3) (4) (5) (6) (7) (8) (Intercept) 0.917 (0.535)* 0.718 (0.548) 0.648 (0.556)* 0.915 (0.544)* 0.813 (0.539) 0.780 (0.550) 0.783 (0.543) 0.799 (0.543) Sub-Sectors 1.367 (0.366)*** 1.244 (0.371)*** 1.337 (0.375)*** 1.260 (0.367)*** 1.335 (0.369)*** 1.227 (0.368)*** 1.316 (0.368)*** 1.238 (0.369)*** Size - 0.186 (0.356) 0.193 (0.372) 0.111 (0.373) 0.161 (0.368) 0.239 (0.363) 0.252 (0.364) 0.251 (0.367) 0.262 (0.366) Year - 0.313 (0.361) - 0.317 (0.369) - 0.344 (0.367) - 0.333 (0.362) - 0.343 (0.366) - 0.293 (0.363) - 0.264 (0.366) - 0.287 (0.362) Ownership 1.027 (0.425)** 0.927 (0.439)** 0.980 (0.444)** 0.929 (0.436)** 0.984 (0.429)** 1.009 (0.439)** 0.939 (0.431)** 1.003 (0.431)** Suppliers 2.226 (0.735) *** Competitors 2.432 (0.735)*** 1.399 (0.742)* Consultants 1.505 (0.742)** Private R&D 16.298 (784.883) Universities 16.357 (783.188) Government 1.367 (0.745) * AIC 287.002 271.772 267.340 283.787 282.778 276.778 276.073 284.161 BIC 307.493 296.361 291.929 308.376 307.377 301.367 300.661 308.750 Log Likelihood - 138.502 - 129.886 - 127.670 - 135.894 - 135.389 - 132.389 - 132.036 - 132.081 Deviance 277.002 259.772 271.340 271.787 270.778 264.778 264.073 272.161 Number Obs. 445 445 445 445 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.18: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. (1) (2) (3) (4) (5) (6) (7) (8) (Intercept) 0.717 (0.374)* 0.659 (0.377)* 0.613 (0.381)* 0.718 (0.375)* 0.692 (0.376)* 0.692 (0.377)* 0.690 (0.376)* 0.714 (0.375)* Sub-Sectors 0.611 (0.215)*** 0.561 (0.217)*** 0.564 (0.219)*** 0.601 (0.217)*** 0.594 (0.216)*** 0.561 (0.217)*** 0.588 (0.216)*** 0.607 (0.219)*** Size - 0.097 (0.225) - 0.116 (0.228) - 0.180 (0.232) - 0.105 (0.227) - 0.096 (0.227) - 0.106 (0.228) - 0.096 (0.227) - 0.096 (0.226) Year - 0.397 (0.231)* - 0.394 (0.232)* - 0.410 (0.234)* - 0.399 (0.232)* - 0.410 (0.232)* - 0.396 (0.232)* - 0.387 (0.232)* - 0.396 (0.231)* Ownership 0.213 (0.309) 0.158 (0.312) 0.118 (0.315) 0.201 (0.310) 0.185 (0.310) 0.188 (0.311) 0.162 (0.311) 0.211 (0.309) Suppliers 0.489 (0.250)** Customers 0.915 (0.259)*** Competitors 0.103 (0.291) Consultants 0.416 (0.308) Private R&D 0.561 (0.343) Universities 0.577 (0.343) * Government 0.033 (0.292) AIC 531.067 529.068 519.403 532.941 531.148 530.148 529.989 533.054 BIC 551.558 553.657 543.992 557.529 555.736 554.756 554.577 557.643 Log Likelihood - 260.534 - 258.534 - 253.702 - 260.470 - 259.574 - 259.084 - 258.994 - 260.527 Deviance 521.067 517.068 507.403 520.941 519.148 518.168 517.989 521.054 Number Obs. 445 445 445 445 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
197
Table 6.19: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. (1) (2) (3) (4) (Intercept) 0.917 (0.535)* 0.600 (0.556) 0.915(0.544)* 0.658 (0.550) Sub-Sectors 1.367 (0.366)*** 1.309 (0.376)*** 1.260 (0.367)*** 1.277 (0.370)*** Size 0.186 (0.356) 0.123 (0.374) 0.161 (0.368) 0.351 (0.375) Year - 0.313 (0.361) - 0.330 (0.369) - 0.333 (0.362) - 0.318 (0.369) Ownership 1.027 (0.425)** 0.986 (0.443)** 0.929 (0.436)** 0.945 (0.437)** Vertical Collaboration 2.899 (0.873)*** Horizontal Collaboration 1.399 (0.742)* Institution Collaboration (Knowledge Provider) 4.501 (1.771)** AIC 287.002 265.926 283.787 274.346 BIC 307.493 290.515 308.376 298.934 Log Likelihood - 138.501 - 126.963 - 135.894 - 131.173 Deviance 277.002 253.926 271.787 262.346 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.20: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. (1) (2) (3) (4) (Intercept) 0.717 (0.374)* 0.624 (0.379)* 0.718(0.375)* 0.681 (0.377)* Sub-Sectors 0.611 (0.215)*** 0.552 (0.218)** 0.601 (0.217)*** 0.568 (0.217)*** Size - 0.097 (0.225) - 0.150 (0.230) - 0.105 (0.227) - 0.092 (0.227) Year - 0.397 (0.231)* - 0.400 (0.313)* - 0.399 (0.231)* - 0.396 (0.232)** Ownership 0.213 (0.309) 0.130 (0.271) 0.201 (0.310) 0.176 (0.311) Vertical Collaboration 0.789 (0.271)*** Horizontal Collaboration 0.103 (0.291) Institution Collaboration (Knowledge Provider) 0.576 (0.398) AIC 531.067 523.942 523.941 530.828 BIC 551.558 548.530 557.529 555.416 Log Likelihood - 260.534 - 255.971 - 260.470 - 259.414 Deviance 521.067 511.942 520.941 518.828 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
198
6.5.3 Probit Model
The research results of the probit model analysis are shown in Tables 6.21 to 6.26, and again the overall results are very similar to the logit model findings, indicating that the analysis results are robust.
Table 6.21: Logistic regression analysis (weighted results) of TC-output and IOC (breadth and depth). (1) (2) (3) (4) (Intercept) 0.917 (0.535)* 0.712 (0.552) 0.648 (0.554) 0.649 (0.554) Sub-Sectors 1.367 (0.366)*** 1.325 (0.373)*** 1.269 (0.374)*** 1.268 (0.374)*** Size 0.186 (0.356) 0.080 (0.368) 0.191 (0.378) 0.211 (0.383) Year - 0.313 (0.361) - 0.378 (0.366) - 0.328 (0.371) - 0.319 (0.372) Ownership 1.027 (0.425)** 0.933 (0.438)** 0.886 (0.441)** 0.890 (0.441)** IOC-Breadth 1.352 (0.442)*** - 0.184 (0.589) IOC-Depth 4.862 (1.569)*** 5.269 (2.088)** AIC 287.002 277.717 267.478 269.382 BIC 307.493 302.306 292.066 298.068 Log Likelihood - 138.501 - 132.859 - 127.739 - 127.691 Deviance 277.002 265.717 255.478 255.382 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.22: Logistic regression analysis (weighted results) of TC-input and IOC (breadth and depth). (1) (2) (3) (4) (Intercept) 0.717 (0.374)* 0.611 (0.390) 0.664 (0.378)* 0.618 (0.396) Sub-Sectors 0.611 (0.215)*** 0.532 (0.223)** 0.549 (0.218)** 0.620 (0.230)*** Size - 0.097 (0.225) - 0.313 (0.240) - 0.119 (0.229) - 0.397 (0.242)* Year - 0.397 (0.231)* - 0.481 (0.240)** - 0.398 (0.232)* - 0.532 (0.243)** Ownership 0.213 (0.309) 0.001 (0.324) 0.143 (0.313) 0.029 (0.328) IOC-Breadth 1.386 (0.274)*** 2.636 (0.532)*** IOC-Depth 0.815 (0.393)** - 2.186 (0.701)*** AIC 531.067 504.285 528.440 495.762 BIC 551.558 528.873 553.029 524.449 Log Likelihood - 260.534 - 246.142 - 258.220 - 240.881 Deviance 521.067 492.285 516.440 481.762 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1%
199
Table 6.23: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. (1) (2) (3) (4) (5) (6) (7) (8) (Intercept) 0.917 (0.535)* 0.718 (0.548) 0.648 (0.556)* 0.915 (0.544)* 0.813 (0.539) 0.780 (0.550) 0.783 (0.543) 0.799 (0.543) Sub-Sectors 1.367 (0.366)*** 1.244 (0.371)*** 1.337 (0.375)*** 1.260 (0.367)*** 1.335 (0.369)*** 1.227 (0.368)*** 1.316 (0.368)*** 1.238 (0.369)*** Size - 0.186 (0.356) 0.193 (0.372) 0.111 (0.373) 0.161 (0.368) 0.239 (0.363) 0.252 (0.364) 0.251 (0.367) 0.262 (0.366) Year - 0.313 (0.361) - 0.317 (0.369) - 0.344 (0.367) - 0.333 (0.362) - 0.343 (0.366) - 0.293 (0.363) - 0.264 (0.366) - 0.287 (0.362) Ownership 1.027 (0.425)** 0.927 (0.439)** 0.980 (0.444)** 0.929 (0.436)** 0.984 (0.429)** 1.009 (0.439)** 0.939 (0.431)** 1.003 (0.431)** Suppliers 2.226 (0.735) *** Competitors 2.432 (0.735)*** 1.399 (0.742)* Consultants 1.505 (0.742)** Private R&D 16.298 (784.883) Universities 16.357 (783.188) Government 1.367 (0.745) * AIC 287.002 271.772 267.340 283.787 282.778 276.778 276.073 284.161 BIC 307.493 296.361 291.929 308.376 307.377 301.367 300.661 308.750 Log Likelihood - 138.502 - 129.886 - 127.670 - 135.894 - 135.389 - 132.389 - 132.036 - 132.081 Deviance 277.002 259.772 271.340 271.787 270.778 264.778 264.073 272.161 Number Obs. 445 445 445 445 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.24: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. (1) (2) (3) (4) (5) (6) (7) (8) (Intercept) 0.717 (0.374)* 0.659 (0.377)* 0.613 (0.381)* 0.718 (0.375)* 0.692 (0.376)* 0.692 (0.377)* 0.690 (0.376)* 0.714 (0.375)* Sub-Sectors 0.611 (0.215)*** 0.561 (0.217)*** 0.564 (0.219)*** 0.601 (0.217)*** 0.594 (0.216)*** 0.561 (0.217)*** 0.588 (0.216)*** 0.607 (0.219)*** Size - 0.097 (0.225) - 0.116 (0.228) - 0.180 (0.232) - 0.105 (0.227) - 0.096 (0.227) - 0.106 (0.228) - 0.096 (0.227) - 0.096 (0.226) Year - 0.397 (0.231)* - 0.394 (0.232)* - 0.410 (0.234)* - 0.399 (0.232)* - 0.410 (0.232)* - 0.396 (0.232)* - 0.387 (0.232)* - 0.396 (0.231)* Ownership 0.213 (0.309) 0.158 (0.312) 0.118 (0.315) 0.201 (0.310) 0.185 (0.310) 0.188 (0.311) 0.162 (0.311) 0.211 (0.309) Suppliers 0.489 (0.250)** Customers 0.915 (0.259)*** Competitors 0.103 (0.291) Consultants 0.416 (0.308) Private R&D 0.561 (0.343) Universities 0.577 (0.343) * Government 0.033 (0.292) AIC 531.067 529.068 519.403 532.941 531.148 530.148 529.989 533.054 BIC 551.558 553.657 543.992 557.529 555.736 554.756 554.577 557.643 Log Likelihood - 260.534 - 258.534 - 253.702 - 260.470 - 259.574 - 259.084 - 258.994 - 260.527 Deviance 521.067 517.068 507.403 520.941 519.148 518.168 517.989 521.054 Number Obs. 445 445 445 445 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1)
200
Table 6.25: Logistic regression analysis (weighted results) of TC-output and innovation collaboration depth. (1) (2) (3) (4) (Intercept) 0.917 (0.535)* 0.600 (0.556) 0.915(0.544)* 0.658 (0.550) Sub-Sectors 1.367 (0.366)*** 1.309 (0.376)*** 1.260 (0.367)*** 1.277 (0.370)*** Size 0.186 (0.356) 0.123 (0.374) 0.161 (0.368) 0.351 (0.375) Year - 0.313 (0.361) - 0.330 (0.369) - 0.333 (0.362) - 0.318 (0.369) Ownership 1.027 (0.425)** 0.986 (0.443)** 0.929 (0.436)** 0.945 (0.437)** Vertical Collaboration 2.899 (0.873)*** Horizontal Collaboration 1.399 (0.742)* Institution Collaboration (Knowledge Provider) 4.501 (1.771)** AIC 287.002 265.926 283.787 274.346 BIC 307.493 290.515 308.376 298.934 Log Likelihood - 138.501 - 126.963 - 135.894 - 131.173 Deviance 277.002 253.926 271.787 262.346 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
Table 6.26: Logistic regression analysis (weighted results) of TC-input and innovation collaboration depth. (1) (2) (3) (4) (Intercept) 0.717 (0.374)* 0.624 (0.379)* 0.718(0.375)* 0.681 (0.377)* Sub-Sectors 0.611 (0.215)*** 0.552 (0.218)** 0.601 (0.217)*** 0.568 (0.217)*** Size - 0.097 (0.225) - 0.150 (0.230) - 0.105 (0.227) - 0.092 (0.227) Year - 0.397 (0.231)* - 0.400 (0.313)* - 0.399 (0.231)* - 0.396 (0.232)** Ownership 0.213 (0.309) 0.130 (0.271) 0.201 (0.310) 0.176 (0.311) Vertical Collaboration 0.789 (0.271)*** Horizontal Collaboration 0.103 (0.291) Institution Collaboration (Knowledge Provider) 0.576 (0.398) AIC 531.067 523.942 523.941 530.828 BIC 551.558 548.530 557.529 555.416 Log Likelihood - 260.534 - 255.971 - 260.470 - 259.414 Deviance 521.067 511.942 520.941 518.828 Number Obs. 445 445 445 445 * Significant at the 10% level (p<0.01). ** Significant at the 5% level (p<0.05). *** Significant at the 1% level (p<0.1).
6.6 Chapter Summary
In summary, this section (quantitative findings) provides an overview of the results of data analysis in support of the ten hypotheses proposed in Chapter 3. The conceptual framework for this thesis links IOC and TC building in emerging economies. The primary data was drawn from
MNSI-6 and used to answer the main research question: the impact of IOC with external partners on TC building in Malaysia.
Quantitative analysis of the research variables was illustrated in both statistical and graphic form.
Issues commonly faced in quantitative studies, such as model specification errors or model fit for
201
dichotomous or categorical data, were discussed. This involved how logistic regression used the best model fit to present the analysis of the research hypotheses using “R” statistical software; and how the model specification of regression equations simultaneously gave equations of the logit model for the ten hypotheses. The chapter also discussed the findings of the analysis of variance performed to test and validate the reliability and validity of the TC building from input and output perspectives.
The main hypotheses of this study (H1 and H2) are strongly supported by the logit model; both
IOC-breadth and IOC-depth are significantly important for TC building in emerging economies.
However, H3 is only partially supported: TC output is strongly supported, but TC input is not supported.
Furthermore, hypotheses H4-H10 focus on the impact of different organization partners (IOC- depth). Only H4 and H5 are strongly supported by the logit model, showing that both vertical partners (customers and suppliers) are important for TC development. On the other hand, H6, H7,
H9 and H10 are partially supported by logistic regression. In contrast, H8 is rejected, as both TC output, and TC input analysis are statistically not significant. There was no association between private R&D institutes and TC building.
202
CHAPTER 7: QUALITATIVE FINDINGS
7.1 Introduction Chapter 6 discussed the quantitative findings of the ten hypotheses testing, and robustness and alternative models that relevant to this research. This chapter divided into two major sections. The first section (7.2) presented the interview results to validate the quantitative finding from Chapter
6 and also provide an in-deep understanding of research questions. The second section (7.3) discussed three mini case studies, which developed based on three individual firms from fifteen firms. The main reason to deployed three case studies to show how Malaysian manufacturing firms are implementing collaborations when dealing with the issue of building technological capability via depth or breadth of partner relations. The three mini case studies provided a comprehensive picture of how individual firms building their TC through external collaboration.
7.2 Interview Results This section discusses the qualitative results obtained from the semi-structured interviews that were conducted to support the quantitative results. They are expected to provide in-depth insight and further explanation of the relationship between the dependent and independent variables. The selection of interviewees and NVivo were used to manage and analyze interviews data are discussed in Chapter 4. This section discussed based on the central issues and sub-issues and used to address the ten hypotheses of the study (see Table 7.1). There are eight issues highlighted in the following sections. The Demographic details of the interviewees with 15 firms and two policy- makers are given in Table 4.8.
7.2.1 Issue: IOC-breadth (H1)
Similar to the quantitative findings, the interview results indicate that IOC-breadth is significantly important for TC development among manufacturing firms in Malaysia. Firms collaborate with a
203
vast number of external organizational partners, allowing them to access new knowledge and tacit knowledge, with which to expand new products and services. Further, the majority of interviewees pointed out that they collaborated widely with various partners to obtain new technologies that are not available internally; such co-operation is also crucial in dealing with risk and uncertainty in the highly technical and dynamic market environment in Malaysia. An interview with the Head of the R&D Department of an electrical equipment company pointed out that:
“In order to speed up our new product and process development, we collaborate with a
larger numbers of external partners as well as with new partners to search the resources
and knowledge that our company require”. (Company Code: EELHT-2)
Another Senior Manager of an R&D Department in a technology and electronic firm also said:
“Even though we have key strategic partners, our company also aggressively engages with
new external partners to obtain the resources and technologies that are not available
internally, which is necessary to develop our company’s technical capability. Collaborating
with new partners increases the possibility of our company accessing new technologies and
technical information that is not able from existing partners”. (HELHT-8)
These quotations and the overall interview results demonstrate that breadth-IOC with diverse external partners can influence a firm’s product and service development and add distinctive new variations to existing knowledge. This enables them to expand their business and introduce new product lines or develop their TC. The findings suggest that IOC-breadth is important for firms’
R&D activities, allowing them to overcome the internal shortage of resources and expertise.
204
7.2.2 Issue: IOC depth (H2)
The interview results suggested that collaboration with specific channels of partners can lead to the acquisition of in-depth knowledge and key information from several specialized technological areas. Such knowledge and information are crucial to developing the firm’s core competence and technological advance. Building strong networks with different external organizations is vital to access key technological resources and obtain specialized knowledge that can aid the technological learning and TC building. In-depth co-operation with the same partners can boost the learning experience and provide a better understanding among managers of product development processes. The Director of one of the companies said:
“Overall, our company is engaged with a large number of partners; however, for all our
corporate offices’ or even home offices’ development or improvement of furniture products
we mostly collaborate with our strategic partners. This is mainly because we have known
these partners or organizations for a long time and the learning process from them is much
easier than with unknown or new partners”. (FCLLMT-12)
The interview findings also revealed that collaborating with the same partners over time it increases firms’ experience and learning opportunity, providing greater advantages to develop their TC. At the same time, this experience supports managers in dealing with inter-firm differences and managing product development, as well as effectively tackling the problems related to improving processes. The Managing Director of an electronic company stated that:
“Collaborating with our existing partners provides us with more flexibility and effectiveness
in our products development. Those partners know us well and our expectation from the
partnership. The experience helps our company to develop our products, processes and
introduce new technologies faster. Our latest GPS device with 360-degree camera was
developed through collaboration with our existing partners”. (ESMEHT-5)
205
Overall, the interview results suggest that IOC-depth is essential to draw more deeply from the key sources of knowledge from familiar organizational partners within a particular technological or application domain for TC development. Further, IOC-depth with the same partners increases the learning experience and provides a better understanding among managers on product and process development overall.
7.2.3 Issue: IOC-depth over IOC-breadth (H3)
The interview results strongly supported the hypothesis that IOC-depth is more important than
IOC-breadth for TC development. Several senior managers commented that their firms collaborated intensively with specific partners (also referring to them as strategic or existing and experienced partners) to develop their TC for key products, services and processes. For example:
“Even though my company co-operates with several external partners, for our GPS device
and latest technological development, we only c-operate with our strategic partner. We have
co-operated with them for a long time and they know what our expectation is in terms of
new device development. At the same time, there is mutual trust between us and our
strategic partners, which prevents our key knowledge and our latest product development
information leaking to competitors”. (ESMEHT-5)
Another senior manager noted that:
“We mainly collaborate with our existing partners for major developments and products
design. Our existing partners provide us with valuable input and flexibility to develop the
products and processes. The cost of collaboration with our existing partners is much lower
compared to new partners”. (FCLLMT-12)
206
A depth of association with certain numbers of organizations will increase the external learning experience and access to key technological resources and specialized knowledge which are crucial in overcoming problems and challenges related to innovation and technological development.
Another senior manager from a high-technology company made an interesting comment on this point, saying that he could build a trustworthy network with intense interaction between specific partners.
“For our key hardware and electronic components development, we only engage with
certain organizations, whom we refer to as strategic partners. This allows us to share our
key technology and knowledge with our strategic partner. It prevents knowledge leakage.
Our strategic partners have more experiences with us in developing products and processes,
which allow us to develop new hardware and electronics components much faster and more
efficiently”. (HELHT-8)
The interview results indicate that IOC-depth is more important than breadth for firms to develop new or significantly improved products or services and processes, and also important for firms’
R&D activities related to TC development. At the same time, IOC-breadth is essential for R&D activities, which allows firms to overcome the internal shortage of resources and expertise in developing new products.
7.2.4 Issue: collaboration with customers & suppliers (H4 and H5)
The interview results show that collaboration with customers and suppliers is important for TC development. The majority of intervieews confirmed that both are key partners for accessing technological knowledge and resources, for their innovation activities and TC development. This is because IOC-depth with customers and suppliers allowing them to access information about
207
customers’ dynamic needs, understand the markets better, and improve the success of new technologies and innovation. It is the shortest route to getting to know competitors’ latest movements on new product development and technology.
For example, one interviewee noted that:
“Customers and suppliers are one of the most important partners to develop our embedded
computer products and processors. Customers and suppliers provide us with the
information that is related to market needs and current products in the market, which is the
key differentiator between our product line and competitors’ product lines”. (HELHT-3)
Constant interaction with customers, especially strategic ones, enables firms to understand their needs and preferences, which is important in upgrading and adding value to existing products and services. This also allows firms to make a significant improvement to their existing and new products and services from input from strategic customers. Another interesting finding was that
IOC-depth with customers allows firms to access competitors’ product information and specifications, to take into consideration when developing new or improved products or services.
As one interviewee said:
“We frequently talk to our strategic customers to develop and improve products or services.
They provide us with important information and valuable customer feedback to improves
existing products and also develop new products. Our strategic customers often share their
expectations in relation to how we could improve our current products . For example, we
are developing DriverLinc based on customer problems and their feedback”. (SCSMEHT-9)
208
Co-operation with suppliers enables firms to learn and understand the new technologies and innovation in markets and helps them to apply the ideas in production processes and manufacturing activities. Nearly all the interviewees agreed on this point.
As one interviewee said:
“Our suppliers know about the latest market demand of customers and trends in the market
(or the market progress). From there we know what products can meet the market demand
and customers’ needs. The suppliers have the technologies and experience related to new
products, so we take the opportunity to learn about their new technology, the method of
producing new products and the processes of producing the new products. Suppliers have
the new or latest technologies that do not exist in the market. We collaborate closely with
them in order to transfer the same technologies to our production line to develop our
products”. (CLHT-10)
7.2.5 Issue: collaboration with competitors (H 6)
The interview results reveal three sets of answers. First, most senior managers did not support the idea of IOC-depth with competitiors as they were unwilling to share their own TC or R&D activities. The interviews reflect the risk and complexity of relationships with competitors.
“We don't have relations with our direct competitor companies. This is because of our
company policy and we are not interested in taking any kind of risk in dealing with
competitor companies”. (ESMEHT-5)
Further, this kind of relationship can lead to knowledge leakage about firms’ key products. One interviewee noted that:
209
“Collobarating with competitor firms involves high risk and also our key technology and
products information may leak to them. That is why our company will not collaborate with
competitor firms at all”. (SCSMEHT-9)
The Senior Manager of an R&D Department also said:
“Competitor firms are involved in the same type business, and their expertise and most of
the technology or technical aspects are very similar to what we have in our company, which
makes no sense for our company to collaborate with them”. (HELHT-8)
The second set of interviewees, from SMEs, admitted that manufacturing firms actively collaborate with competitors to utilize the R&D facilities and reduce to the cost of innovation activities. One manager justified this view by pointing out the following:
“We collaborate with other cosmetic companies in Malaysia mainly to deal with cost
reductions of our product development. Since we do not have our own R&D department,
relationship with competitors give us access to their R&D facilities and alsoenables us to
learn their production and manufacturing processes such as standard operating procedures
(SOP)”. (HSMELMT-4)
Thirdly, the other important findings pointed out from the interviewees showed that firms actively collaborate with similar type of companies that are not direct competitors, to develop their TC, especially for new products or services and processes. This co-operation provides them with a different dimension of knowledge and technology, enabling them to build stronger TC and also improve their marketing-related activities. Further, interview results suggested that firms actively collaborate with complementors (firms that have mutual benefits) for their technological development. Mainly, the complementor firms (complementary products) creates a win-win
210
situation for both companies in developing new products or services that offer to the customers.
This allowed both firms to exchange their knowledge and expertise to build their TC.
The Managing Director of an electronics components and GPS company put forward the following argument:
“We often collaborate with similar types of companies to us. This type of partnership is
crucial for the nature of our business, and also provides a new way to develop products like
hardware and software. These partners help our company to learn about new technology
and expertise that we do not have in our company. At thehe same time, these relationships
allow us to develop new products or services, new markets and also new to the world”.
(ESMEHT-5)
Another senior manager said that:
“Our company frequently co-operates with companies that are in the same line of business;
however, these companies are not our competitors as they target different market segments
and customers. Co-operating with these companies allows us to make new products that
increase our market share and also tap into new markets and customers. These kinds of
partnership allow us to combine different product knowledge and technologies into our
products to make new products and services”. (FCLLMT-12)
Overall, the majority of manufacturing firms are not willing to collaborate with their direct competitors to develop their TC, for either R&D activities or product or service or process development. However, some interviewees especially from SMEs, believe in co-operating with competitors to reduce R&D costs and risk; it is an important mechanism for innovation and TC
211
building. The third important finding shows that firms actively collaborate with similar types of company to develop their TC.
7.2.6 Issue: consultants and private R&D institutes (H7 and H8)
The interview results strongly support collaboration with consultants (hypothesis 7), but not with private R&D institutes (hypothesis 8). The interviewees claim that in-depth collaboration with consultants is vital to access specialized knowledge, skills and the latest technology. The results also reveal that co-operation with consultant is more relevant in manufacturing firms to implement new technology, new production systems and transfer their skills and knowledge to the organization.
The Head of an R&D Department justified this by pointing out the following:
“Consultants are crucial for us because they know the market demand. They are very
experienced about the market, new products and latest technology in the market that is
related to snacks production. We have a strong relationship with them because we need
their experience and expertise and also knowledge to develop our existing products and
develop new product lines”. (FBLLMT-1)
Consultants can also provide solutions to a wide variety of business-related problems, enabling firms to make decision more efficiently. For instance, the Managing Director of an electronics components and GPS company stated that:
“We only work with consultants when our company is near the deadline or faced with
critical problems to complete the product development. The consultants are able to identify
the problems much quicker and also complete the product development on time. We also
212
learn the latest processes or product developments methods, which has a greater impact on
overall product and process development”. (ESMEHT-5)
In line with statistical analysis in support of hypothesis 8, the interviewees claim that manufacturing firms are not keen to engage with private research institutes to develop their TC. It is costly and the risks of unplanned knowledge spillovers are great. The vast majority of interviewees agreed with this point. As one of the interviewees said:
“Collaborating with private R&D firms is very risky and complex, and co-operating with
them may cause us to lose our key product knowledge and technology to competitors’
companies if the competitors collaborate with the same private R&D companies”.
(FFSMELMT-15)
Another manager stated that:
“My company is small and we don't have large capital to invest in private R&D to develop
our products. That's why we collaborate with universities and government research
institutions as an alternative option”. (FBLLMT-1)
Overall, the interview results reveal that IOC-depth with consultants is considered critically important for manufacturing firms to develop their TC. On the other hand, IOC-depth with private research institutes is not important since the cost of collaboration is high and unplanned knowledge spillovers too dangersous.
213
7.2.7 Issue: universities and government research institutions (H 9 and H10)
In relation to hypotheses 9 and 10, the interview revealed two sets of findings. The first suggests that the majority of interviewees are unwilling to collaborate with universities or government research institutions as they believe that universities lack the requisite practical knowledge and capacity to engage in technological collaboration. Collaboration with government or public research institutions is less beneficial and less effective in terms of TC development, as they try to take advantage of company resources and technologies for their own benefits or pass them on to semi-government companies (government link companies).
One Managing Director summarized this belief as follows:
“I think Malaysian universities are not at international standards compared with world
standings like Singapore universities. In term of research, we are still far behind several
universities in Asia”. (ESMEHT-5)
Another CEO stated that:
“Most of the universities in Malaysia don't have the latest technology or high-level IT
system. No point for our company to invest the money in projects with universities, rather
we develop products from collaborating with other companies”. (ITCSMELMT-14)
Another manager said:
“Government research institutions are not willing to support our MNC company for
innovative projects with international standards; the government focuses on smaller
projects and SMEs”. (ESMEHT-5)
214
The second set of interviewees (mainly SMEs) believed that co-operating with universities and government research institutions was critically important for TC building. SMEs actively engage with them to improve their R&D activities and at the same time develop new products or services and processes.
The CEO of one SME said:
“We mainly collaborate with local universities and government research institutions to
overcome our knowledge shortage and also access technology facilities to further develop
our products with them”. (FBLLMT-1)
Interaction with universities or public research institutions is increasingly seen as an inexpensive approach for SMEs to access scientific knowledge and technologies since more public funding is available for firms to collaborate with them. The Company Director justified this view by pointing out the following:
“Co-operating with universities is a cost-effective option for us compared to private R&D.
For example, some of the materials to develop our products come from universities; the
prices are reasonable and the quality is very high”. (HELHT-3)
The interviewees were divided over collaboration with universities and government research institutions. Only SMEs actively seek to co-operate with them in order to undertake larger innovation projects and also to overcome the limitations of their own R&D.
215
7.2.8 Other Issues
The other significant findings are there are substantial differences between large firms and SMEs in Malaysia in the way develop TC, particular the way they collaborate with external organizations to increased the knowledge base and capability building. Large firms or MNC tend to use both wider and deeper collaboration to increase external knowledge and resources. Big companies have comparatively adequate slack to maintain their pursuit both collaboration strategies concurrently, and they have resources and capital to manage the complexity of collaboration from a wide range of external organizations. Conversely, SMEs relatively experience more resource restrictions, and they may have a compromised between deeper and wider co-operation. The interviews results reveal, SME tends to trade-off wider collaboration
(IOC-breadth) in favour of deeper collaboration (IOC-depth). There are several reasons to explain such action by SMEs.
Interview with Malaysian SMEs revealed that they face numerous challenges to develop their TC.
In particular, they do not have in-house technical support or maintenance support (such as own
R&D department), lack the financial resources, fewer inventive employees, and lack of understanding about the idea generation process or new technologies. For SMEs focusing both breadth and deeper collaboration are more costly, and demand significant efforts and time from managers. Therefore, SMEs focused more on depth collaboration, since concentrating on fever partners is easier to manage and less costly compared to maintaining a broader range of collaboration with external organizations.
216
Table 7.1: Summary of Research Results Hypotheses 1-10 Hypothesis Obtained sign Confirmation TC-Input TC-output H1 Inter-organizational collaboration breadth has positive relationship/association with technological SF SF Supported capability building. H2 Inter-organizational collaboration depth has positive relationship/association with technological SF SF Supported capability building. H3 Inter-organizational collaboration depth has a stronger and positive association with technological N-SF SF Partially Supported capability building compare to inter-organizational collaboration breadth. H4 Inter-organizational collaboration depth with suppliers has a positive association with TC SF SF Supported building. H5 Inter-organizational collaboration depth with customers has a positive association with TC SF SF Supported building. H6 Inter-organizational collaboration depth with competitors has a positive association with TC N-SF SF Partially Supported building. H7 Inter-organizational collaboration depth with consultants has a positive association with TC N-SF SF Partially Supported building. H8 Inter-organizational collaboration depth with private R&D institutes has a positive association N-SF N-SF Not Supported with TC building. H9 Inter-organizational collaboration depth with universities has a positive association with TC SF N-SF Partially Supported building. H10 Inter-organizational collaboration depth with government research institutions has a positive N-SF SF Partially Supported association with TC building. H4-5 Vertical Collaboration SF SF Supported H6 Horizontal Collaboration N-SF SF Partially Supported H7-10 Institution Collaboration (Knowledge Provider) N-SF SF Partially Supported Note: Significant (SF) Not Significant (N-SF)
217
7.3 Case Studies Analysis: Technological Capability Building
Three firms from the manufacturing sector were selected as case studies to analyze and understand the influence of collaboration with external organizations on TC building in emerging economies in a more nuanced way. The firms selected differed in size, sector of origin, nature of the business, year of establishment and country of origin, to generalize the context of Malaysian manufacturing sector. EELHT-2 (Case A) is a large electrical equipment manufacturer and the company located at Kuala Lumpur. The other two firms are SMEs. ESMEHT-5 (Case B) is a foreign subsidiary and manufacturers electronic products related to telematics and Global
Positioning System (GPS). It is headquartered in Singapore while FFSMELMT-15 (Case C) is a
Malaysian owned company and manufacturer of furniture locks. The three case studies illustrate how these firms build TC, in particular, what role they play by wider and deeper collaboration.
The full details of the three firms are shown in Table 7.1. The following section discusses the three case studies.
Table 7.2: Profile of Firms Interviewed for Case Studies. Company Year Local/ Sector Size High-Tech Interviewees Date/mode of Length of Code Established Inter /LMT interview Interview EELHT-2 1994 Local Electrical Large High-Tech 1. Company Director 26-Jun-2016 40 mins (Case A) equipment (In Person) 2. Company CEO 3-Feb-2019 60 mins (In Person) 3. Production Manager 10-Feb-2019 50 mins (In Person) 4. Manager of R&D 10-Feb-2019 90 mins Department (In Person) ESMEHT-5 2004 Inter Electronics SME High-Tech 1. Managing Director 15-Sept-2016 90 mins (Case B) Components (Skype) and GPS 2. Head of Product 15-Feb-2019 30 mins Development (In Person) 3. Production Manager 15-Feb-2019 70 mins (In Person) FFSMELMT-15 1988 Local Furniture SME LMT 1. General Manager 15-Oct-2016 90 mins (Case C) Locks and (Skype) Fittings 2. Production Manager 20-Feb-2019 50 mins (In Person) 3. Marketing Manager 21-Feb-2019 70 mins (In Person)
218
7.3.1 Case Study A: EELHT-2
7.3.1.1 Company profile
The first case firm - EELHT-2 (Case A) was established in 1994. It is an electrical equipment manufacturer, specialized in electrical accessories, test measuring instrument and safety electrical products. In 1994, the company started as a small manufacturer of electrical accessories with less than 50 employees. Today, it is one of the largest company in Malaysia manufacturing electrical products and equipment for the power and utility industry. In 2018, it had approximately 600 employees in Malaysia and other countries. The company’s headquarter and R&D center is located in Kuala Lumpur.36 The company achieved significant growth mainly due to its fully automated production methods and also the use of robotic technologies and computerized system in managing manufacturing processes. The majority of is production of electrical goods is for the domestic market.
Primarily, it focused on supplying electrical products and accessories to leading companies in
Malaysia - public and private utility companies such as Tenaga Nasional Berhad, Telekom
Malaysia Berhad, Sabah Electricity Sdn Bhd, Sarawak Energy Berhad, Petroliam Nasional Berhad
(also known as Petronas), and other corporate companies. Over more than twenty years, the company was, according to the interviewees, able to achieve a large market share of electrical products and accessories in Malaysia and other countries. They have established several brand names, which have penetrated and remained competitive across Malaysia and other international markets. The management suggested that this was an outcome of the primary and longstanding strategy of the firm, which was to secure and enlarge its market position.
36 Kuala Lumpur is the capital of Malaysia.
219
7.3.1.2 TC building
Interviews with company Director, CEO, and Production Manager reveal that TC development is a critical component of competitive advantage and future direction of the company. It incorporates the latest technologies and technical knowledge to improve existing products and develop new electrical products and also upgrade the overall process of production. More specifically, it achieved significant improvements in technical specifications, components, and materials, incorporated software, user-friendliness or other functional characteristics such as using advanced machinery, equipment, and computer hardware or software.
When the company started the business operation in 1994, it had limited capability and capacity to develop electrical equipment on its own. Therefore, the management put significant efforts externally and within the firm to improve their technological capabilities. They heavily invested in in-house R&D (spending around 5-6 percentage of annual turnover in R&D) by focusing on various projects to develop electrical products. At the same time, they also recognized the importance of external collaboration to build and improve existing technological capabilities. In
1996, they signed legal agreements with three companies separately in order to license in their technologies; one from Malaysia and the other two companies from the UK and Japan respectively.37 All three were large MNCs, and the collaborations were short-term agreements, less than two years. This licensing-in strategy was a faster way to obtain a new technology compared to developing it in-house.
The agreement with the Malaysian company provided them with technical knowledge to improve the logistics, delivery and distribution methods for their electrical products. It also allowed them to access information about the Malaysian market in both West and East Malaysia. According to
37 That was the first formal collaboration of their firm with external companies. The company not willing to reveal collaboration companies name due to confidentiality.
220
the CEO, full knowledge about the Malaysian market is crucial for new product development. The collaboration with two overseas companies allowed them to access advanced machinery, equipment, and both computer hardware and software. The alliances with three companies facilitated learning of basic and advanced technical knowledge and expertise to support the internal R&D activities and improve overall technological capabilities. At the end of the two-year term, they renewed the agreement with two of the firms, one from Malaysia and one from Japan.
The CEO indicated that “the two companies provide us with what we need and we have high trust in them. We still collaborate with them until today”. This shows that they have developed very strong collaborative relationships and high mutual understanding with the two companies. Such strong relationships enable firms to achieve more, at a faster rate, and with less cost or risk.
Since the company manufactures electrical products, both price and quality are important for the company’s stability and growth. They put significant attention to develop and manufacture electrical accessories at a competitive price (cost per-unit). Collaboration is an important strategy for them to access external knowledge and information for electrical products development.
According to the CEO and Director, the firm has both long-standing and more recent collaborations with various external partners (i.e. customers, suppliers, consultants, universities, and government research institutes) in Malaysia to access new knowledge, technical expertise and different product materials. New partners provide them with new information or knowledge that is not available internally, while existing or familiar partners provide them with intense technological learning. This allows the company to manufacture high-quality electrical products and accessories at a competitive price.
According to the Production Manager, the company recently entered into several strategic alliances with domestic manufacturers and suppliers to ensure the quality of electrical products
221
development, especially electronic control units and power transformers (some of them are new to the company). It proactively recognized a need early on to acquire new knowledge from different sources and paid significant attention to establishing relations with local and foreign technology partners. The company’s Director claimed that “we currently deal with more than 400 suppliers
(technology providers) in Malaysia and other overseas countries. However, we only have strong strategic alliances with limited numbers of suppliers.” The stronger connections with suppliers enable them to improve and develop their technological and technical advancements.
Further, they set up strategic alliances with several semiconductors, mobiles manufacturers, and electronics companies, with the purpose of deepening knowledge in the field of processor technologies, integrated circuits, data communication, and other emerging technologies. Before it established a formal R&D department, the firm bought production technology licensors and used most advanced technology and engineering facilities from suppliers and consultants. Those partners provide technical support and knowledge to the company in the early stage of company operation. The company began its operation using conventional warehouse systems and management but continually upgraded its technologies and facilities through learning by interacting (basic and intense learning) with technology suppliers, consultants, and customers companies. Currently, it has an advanced warehouse system using robotic technologies, which fully automated and computerized.
For example: According to the Production Manager, in the past, they collaborated with University
Technology Malaysia (UTM)38 , and Standards and Industrial Research Institute of Malaysia
38 Universiti Teknologi Malaysia (UTM) is a Malaysian public research-intensive university in engineering, science, and technology located in Johor. https://www.utm.my/
222
(SIRIM)39 to develop product materials for meter box and electrical suits. Since both of these organisations have the technical expertise and facilities to develop electrical product materials, and this collaboration was seen as an opportunity to develop products at a lower cost. However, this partnership did not succeed in the initial objective of product materials development. Failure was due to high employee turnover, especially several key research assistants leaving the companies, the duration, and process of the project taking longer than expected, and the lack of coordination from UTM and SIRIM. This shows the weakness of public universities and government research institutes that often characterises emerging economies such as Malaysia.
However, the company learned the materials technologies and knowledge from the partnerships, which they successfully used later years to develop cable and cable accessories at a competitive price on their own.
7.3.1.3 IOC and TC building
The case study analysis of the interviews and secondary data40 indicate that IOC with the external partners is an important driver of technological learning and TC building. The firm formed IOC
(both wider and deeper collaboration) with other organizations to create added-value and increase external technological learning. The interviewees suggested the ability of the company to implement and handle technical change in manufacturing plants and processes was significantly improved during their interactions with different external partners: both familiar (frequently collaborate) and non-familiar (newly establish partnership).
The CEO stated: “in the past, we invested heavily in R&D, especially in safety electrical products and test measuring instruments, however, the results not satisfied us. Since then, we reduced our
39 SIRIM is a government research institute; offer various services to assist industry, government, and society for industrial quality betterment. SIRIM owned wholly by the Malaysian Government, under the Ministry of International Trade and Industry (MITI). http://www.sirim.my/ 40 The secondary data consisted of company annual reports, industry websites, industry journals, newspapers, business magazines, and industry association publications.
223
investment in R&D activities and put more focus on external collaboration with local and foreign sources of technologies.” Initially, the company’s R&D investment focused on too many areas
(e.g. large number of projects) and required a high level of coordination and workforce. However, the outcomes were not satisfactory relative to the investment made. Therefore, they decided to progressively reduce the investment and focus more on end product development through strategic alliances, legal agreements, and joint ventures with external companies. According to the company Director, the development of new electrical equipment and the improvement of existing products have been more successful as a result of collaboration. The product development turnaround time is much faster, which leads to products being available in the market more quickly.
Example: Electric meter box and smart meter TC development
According to interviewees, initially the company aspired to acquire access to the basic technologies of the meter box and design through the license agreement in 2000 with two
Malaysian companies: “Spire Metering Technology” 41 and “MISA Sdn Bhd” 42 , which firm managers considered would address their limited technological expertise 43 . As a main technological learning strategy, the company management wanted to learn as much as possible from the relationships with “Spire Metering Technology” and “MISA Sdn Bhd” and accumulate knowledge over time through planned experimentation in successive “meter box” development on a project-by-project basis.
41 Spire Metering Technology is a USA Company and started the operation in Malaysia in 1995. The company manufactures measurement and energy metering products. 42 MISA Sdn Bhd established in 1980 and is a Malaysian based company. The company design develops, manufacture, and marketing of kWh electricity meters, water meters and gas meters for major energy and water utility companies. 43 According to interviewees, both Spire Metering Technology and MISA Sdn Bhd companies are not direct competitors to EELHT-2, since both companies target market and customers are different from EELHT-2. The Company Director said, “we never collaborate with our direct competitors; such partnerships will bring us down.”
224
According to the CEO, the strategic agreement with “Spire Metering Technology” and “MISA
Sdn Bhd”, enabled employees to access a highly skilled pool of expertise and learn about critical elements of advanced meter box technology, designs, including Advanced Metering Infrastructure
(AMI) strategy. The external mediated learning by interacting process occurred during the firm’s site visits, project involvement, and through technical assistance. This learning process was particularly intensive in the period after the second-generation meter box was constructed in 2002 when the management committed to persistently devoting efforts and resources to leverage learning through regular interaction and communication between employees in the respective firms. Through this interactive relationship with two companies and other external partners, the company’s employees reached sufficient level of knowledge to enable them to subsequently manufacture, install, and further develop third and fourth generation meter box independently.
According to the Director, in 2006, the company identified potentials for “Smart Meter” product development in Malaysia. The company collaborated with two Malaysia semiconductors companies, Samsung Malaysia, and “MISA Sdn Bhd” to get access to technological expertise and knowledge to develop “Smart Meter” independently. They formed strategic alliances with the three companies separately. The Production Manager said, “from strategic alliances with semiconductors companies and Samsung Malaysia we get access underlying technology information such as processor, range link, power consumption, and mobile technical expertise like cloud integration, remote monitoring, and real-time update. This allowed us to learn the expertise and knowledge from semiconductors, Samsung Malaysia, and our existing partners to develop
Smart Meter”. This allowed them to gain and learn the necessary knowledge and technology to develop “Smart Meter” on their own.
225
7.3.1.4 Summary
In a nutshell, the analysis of case study A, strongly suggested that IOC with the different partners is essential for technological learning and TC building. The firm gradually generated and improved its TC in the power and utility industries through assimilating and enhancing the technical expertise from collaboration with external partners. The firm considered collaboration as a critical strategy to access the growing and emerging technologies.
In particular, two collaboration strategies were used: wider collaboration (IOC-breadth) and deeper collaboration (IOC-depth) to access knowledge, resources and technical expertise from the external environment. From various partners, collaboration with suppliers, customers, consultants, and similar type firms (but not direct competitors) are highly relevant for the company to build their TC. Both universities and government research institute contributed relatively less to building TC. Past experiences collaborating with universities for materials development of meter box and electrical suits did not succeed. Finally, the firm was reluctant to establish any relationships or partnerships with direct competitors or private R&D firms for TC development due to the risk and complexity of the relationship and unplanned knowledge leaked are high.
7.3.2 Case Study B: ESMEHT-5
7.3.2.1 Company profile
The second case firm - ESMEHT-5 (Case B) is a well-known company in manufacturing electronics products related to telematics and Global Positioning System (GPS). The firm specializes in designs and development GPS device including Enterprise Management Software
(EMS) and advanced location-based IT services, and solutions that combine GPS positioning, wireless communications and mapping technology. It was established in 2004 and located in
Johor state in southern Malaysia and linked to Singapore. In 2018, around 55 employees were
226
working with the company. The company headquarters are located in Singapore, but has major operations in Asia-Pacific, Europe, America, and the Middle East. However, the operations in
Malaysia is very different from Singapore. In Malaysia, the company developed its own R&D center which services MNCs, enterprise and government customers spanning numerous regions and industry sectors.
At the beginning of its business operation, it was an electronic parts manufacturer of telematics and GPS products. Most of the production parts and accessories were exported to countries like
Singapore, Europe and the United States of America (USA). After a few years of operation, it started to recognize the business opportunities and high demands for telematics and GPS devices in Malaysia and other Asia countries. In 2007, the company began to develop telematics and GPS products and slow down the manufacturing of electronic parts. Since then, it has launched different generations of telematics and GPS devices in Malaysia and other countries. Today, the company is one of the largest manufacturers of telematics and GPS devices in Malaysia.
7.3.2.2 TC building
The interview results indicate that TC building is an important element of their firm growth and competitiveness. According to the Managing Director, it aims to: “provide an effective product solution that meets our consumer preferences and also develop new products that are new to the market. We always look at new technologies and expertise to improve our existing products and also develop new products and services.” This statement indicate that they put significant efforts to incorporate the latest technologies to develop new products and improve the methods of manufacturing, including logistics, delivery, and distribution of inputs, goods or services.
227
In 2007, the company started the production of telematics and GPS products for the first time in
Malaysia. However, they faced numerous challenges - particularly lack of TC and resources to develop products internally. According to the company Director, to overcome their weaknesses, they entered into agreements with firms from Japan, USA, and the United Kingdom, as well as with local companies to acquire technological capabilities. Firms from those countries are leading manufacturing firms related to telematics and GPS products. Initially, they signed licensing-in agreements with these lasting two to three years. These arrangements allowed the company and its staff to obtain the necessary skills, complementary assets, and resources more quickly than developing them internally. At the same time, they also collaborated with the parent company
(headquarters) from Singapore. Such collaborations allows them to access their experience and technological expertise to improve business operation and product development. The legal agreements with external companies and collaborating with the parent company (within company group) enabled them to develop telematics and GPS products and improve overall manufacturing processes such as maintenance systems, logistics, delivery, and distribution methods in 2008.
7.3.2.3 IOC and TC building
The analysis of the case study indicates IOCs with the external organizations are important drivers of technological learning and TC building. The company used two collaboration strategies; wider collaboration (IOC-breadth) and deeper collaboration (IOC-depth) to obtain knowledge and resources for their TC development.
The Head of Product Development suggested that: “the key was to build a strong collaborative relationship with the buyers (customers) and suppliers from domestic and overseas markets and sharing their technical knowledge with us.” At the same time, they also have strong collaboration agreements with the partners that they know for a long time and with companies that they
228
frequently collaborate. According to the Managing Director, they currently work with over 250 suppliers compared to 10 suppliers in 2004. However, some of the suppliers are very important to them in obtaining key technological knowledge.
In 2010, the firm formed a joint venture with two local companies to produce a microprocessor in
Malaysia. The two local companies are medium-size firms that manufacture various electronic devices44, both of whom were previous collaborators and with whom high mutual understanding and trust had developed. This joint venture was set up with the strategy to expand new product lines and obtain advanced technologies.
Example: 7th Generation GPS Device TC development
The first generation of GPS devices was successfully developed and introduced to the market in
1996. According to the Managing Director, the first version of GPS development was very challenging since the company lacked capacity to build the GPS production line. Therefore, they formed collaborations with both within-group companies (parent and subsidiary firms) and external partners. The Managing Director suggested that “without strategic alliances with external companies, we may not have developed the GPS device on time. Similarly, our group companies play an important role in build-up GPS devices in 1996.” Both wider (IOC-breadth) and deeper collaboration (IOC-depth) are significantly important to the company to access different sources of knowledge and expertise, and technological learning.
Since then, they made continuous improvements to their GPS products. In 2000 they unveiled the second generation GPS and this was followed subsequently by the third, fourth, fifth and sixth generation of GPS devices. These improvements mainly came in three ways. First, the company
44 The company not willing to reveal joint venture companies names due to confidentiality.
229
formed various forms of collaboration with external companies such as strategic alliances, joint ventures, licensing agreements, collective research organization and others. Second, it benefited from collaboration within the group company, especially with the parent company (from
Singapore). Third, internal efforts and R&D to maximize the use of external knowledge and technological learning to improve existing products and also develop new GPS devices.
In 2018, they brought the seventh generation of GPS devices to market in Malaysia and other countries. The seventh generation of GPS devices included the latest technologies, including 4G, microprocessors. The Head Product Development claimed that collaboration is the primary strategy for their company to access the latest technologies, resources and technical knowledge that not available internally.
Interviews with the Managing Director, Head of Product Development and Production Manager suggested that the latest GPS product development resulted from collaborations with key customers, suppliers, consultants, universities, government research institutes, collective research organizations (CREST), complementors (firms that have complementary benefits), organizations that offer similar products in different markets, and organizations that offer different products in same markets. The company strongly believed that collaboration with diversified channels brought more valuable knowledge and resources. Among the key collaborators were the organization for Collaborative Research in Engineering, Science and Technology (CREST).
CREST was established in 2012 as a government initiative to accelerate Malaysia’s economic growth by creating a vibrant R&D ecosystem for the electrical and electronics (E&E) industry and brings together industry-academia for collaborative R&D. The firm was one of the founding
230
members and has collaborated with CREST since 2014.45 This co-operation allowed them to engage with leading E&E companies, government bodies, and top universities in Malaysia which in turn provides greater opportunities to access the latest technologies like “3G”, “4G” 46 , microprocessors, the latest Bluetooth or Wi-Fi communications software and hardware, flexible customization through built-in programmable rules engine and control automation capabilities.
In addition, they formed strategic alliances with complementors (firms that have mutual benefits) such as automotive, truck manufacturers, app developer, mobile companies and others. For instance, they entered into legal agreements with truck manufacturers and automotive companies to develop GPS navigation products as portable or built-in for both trucks and cars, and “Rear
View Camera” accessories, where drivers are able to use the GPS navigator and multimedia display to see back of the vehicles while reversing. The automotive companies provided them with car-related technologies, while mobile companies provide 3G/4G technology and app developer provide knowledge on iOS, Android, Windows, App Store, Platforms, HTML5, 3D and others. These offered them greater learning opportunities to learn the latest technologies from the respective company’s.
The company considers collaborations with organizations that offer similar products in different markets and organizations that offer different products in the same markets are equally important as other partners. However, both types of companies are not direct competitors. According to the
Managing Director, collaboration with direct competitors is too risky, and there is the possibility of knowledge leakage to competitors. They engaged with organizations from different markets
45 CREST’s 15 Founding Members include 11 leading E&E companies namely Advanced Micro Devices (AMD), Altera, Avago Technologies, Bose, Clarion, Intel, Keysight Technologies, Motorola Solutions, National Instruments, OSRAM Opto Semiconductors, and SilTerra; together with the Northern Corridor Implementation Authority (NCIA), Khazanah Nasional, University of Malaya (UM), and University of Science Malaysia (USM). 46 “G” stands for a generation of mobile technology, installed in phones and on the telecommunication network.
231
mostly from overseas countries; by contrast, they collaborate with organizations in the same market, mainly from Malaysia. The collaboration with both types of organizations led to them obtaining obtain similar but complementary technologies from the overseas and domestic market and allowed them to upgrade their existing technologies to facilitate the latest GPS product development.
7.3.2.4 Summary
The analysis of case study B indicated that IOC with the different organizations is an important driver of technological learning and TC building. They formed collaborations with customers, suppliers, consultants, collective research organizations (CREST), complementors (firms that have complementary/mutual benefits), organizations that offer similar products in different markets, and organizations that offer different products in same markets. The interviews result and secondary data reveal that the company employed both wider (IOC-breadth) and deeper (IOC- depth) collaboration to upgrade their TC. The wider collaboration allows them to access new knowledge and technologies that not available internally, while deeper collaboration provides them with an excellent learning opportunity to obtain key resources and technical knowledge from familiar partners. These enabled the company to upgrade the production capacity and manufacturing processes such as maintenance systems, logistics, delivery, distribution methods, and new products development: internal structure, appearance design, and hardware design.
7.3.3 Case Study 3: FFSMELMT-15
7.3.3.1 Company profile
The third case firm - FFSMELMT-15 (Case C) is one of the leading manufacturers of furniture locks in Malaysia. It specialized in producing furniture locks, furniture fittings, cabinet locks, and electronic locks. The firm was founded in 1988 and located in Penang. It started as a small family
232
business, in 2018 it had an estimated 95 employees. The success of the company was sparked off from a small sole proprietorship company in trading furniture hardware, fittings, and accessories.
In 2018, fortified with its factory and affiliates company, it is a far cry from its small beginnings as a trading and manufacturing company. It supplies a wide array of furniture locks under two well-recognized brands. In recent years, the two brands have penetrated and remained competitive across Asia, Europe, Africa, and the Middle East, making its mark in 60 countries and growing.
7.3.3.2 TC building
The interviews with three key managers suggested that TC building is an essential source of businesses growth and competitive advantages of the company. When asked about the company’s competitive advantage, the General Manager replied that the firm considers its quality, reliability, new technologies, competitive cost, experienced workforce, bulk volume handling as its competitive edge. They also claimed to have substantial external linkages, marketing, and a diversified customer base. The company’s vision is to be one of the leading companies in South
East Asia in production of furniture locks and accessories.
In 1988, the company started as a small family business - trading furniture hardware, fittings, and accessories. Later, it became involved in manufacturing furniture locks and other furniture accessories. At the beginning of the business operation, the company faced significant challenges to develop and manufacture furniture locks independently, due to lack of technological capabilities and technical expertise. To overcome technological weakness, they set up strategic alliances in 1995 with two MNC companies from Malaysia and the United Kingdom. Two separate legal agreements with the two MNC companies allowed them to transfer technologies, advanced machinery, equipment, and computer hardware or software, and methods of manufacturing. The collaborations provided opportunities to learn and develop their technical
233
competencies and capabilities to manufacture furniture locks and accessories. In the late 1990s, its technological capability relied mostly on technology licensing and technical knowledge transfers from suppliers, customers, and consultants.
7.3.3.3 IOC and TC building
This case analysis strongly suggested that collaboration with external actors are an important source of knowledge for technological learning and TC building. Collaborations with external organizations like suppliers, customers, consultants, universities, public/government research institutes, and complementors to master the production technology and access to the latest technologies in the market. These allow them to improve existing products and produce new furniture and cabinet locks. The three key managers suggested that both wider and deeper collaboration were significant in developing their TC. Collaborating widely with different organizational channels enhances their knowledge pool by adding distinctive new variations and expands new product lines - electronic and invisible lock. On the other hand deeper collaboration allowed them to access in-depth knowledge and create a proximal experience, with intense technological learning.
According to the company’s CEO, collaboration is an important source of technological knowledge and external learning. Frequent engagement with strategic customers and suppliers allow them to obtain information and knowledge to expand furniture lock and product variety. In a subsequent interview with the Production Manager, he claimed that “technological partnerships
(legal agreements) enable us to learn and implement the learning activities to deepening the technical knowledge for increasing the technological absorption capacities and for promoting the dissemination of technology.” Existing partners or long-term partnerships allowed them to offset internal limitations and provided them with intense technological learning and critical resources.
234
They also form new collaborations with external companies (or firm that they never collaborate or form partnerships before) to access new opportunities or knowledge. The company uses the new source of knowledge and information to develop new products and improve its competitive advantages continually.
Furthermore, the acquired knowledge supported the employee to upgrade the entrepreneurial skills and overall production through a learning process. The CEO claims that: “from 2010 to
2015, our company collaborated with more than 3,000 customers in Malaysia and other overseas countries. Some of them are important to us and provide valuable technical knowledge (e.g. 3D printer technology and production machine) to upgrade existing technology in manufacturing methods.” According to the General Manager, collaborating with universities and government research institutes is considerably, important in improving the logistics, delivery, distribution methods and products materials. They also formed partnerships with local universities and public research institutes, especially with the University of Malaya (UM)47, University of Technology
Malaysia (UTM) and SIRIM to get access to their technical expertise. The interviews reveal that collaboration with government institutes and universities/colleges are easier and more accessible compared to other types of partners. Government institutes and universities have more grants and incentives from the government to collaborate or for joint projects with industries, especially with
SMEs. Similarly, the Marketing Manager claim “universities and government research institutes are keen to collaborate with us. For us to collaborate with them are more secure and less expensive in relevant to the other type of partnerships.”
47 The University of Malaya (UM) is a public research university located in Kuala Lumpur. The University foremost and premier Research University (RU) in Malaysia. https://www.um.edu.my/
235
Example: Electronic and Invisible Locks TC development
In 2016, the company identified the high market demand for electronic and invisible locks in
Malaysia and other overseas countries. They were highly interested to develop both electronic and invisible locks on their own; however, they lacked resources, technical expertise, and the latest technology. The board of directors decided to pursue collaboration as a primary strategy to deal with weak TC, as they could develop electronic locks faster than depending solely on internal
R&D. Therefore, they formed various strategic alliances with different organizations from
Malaysia and overseas countries.
First, they collaborated with customers from Malaysia and other countries. Their customers provided them with valuable information about competitors’ products and also their expectation for the new products. These allow them to analyse the market on existing electronic and invisible locks, and also technological advancements in this spectifi sector. Close association with key customers provided them to access their latest technology and technical knowledge. The
Marketing Director said, “our key customers share their expertise on the remote control, digital conversation system, zinc alloy lock body, and software PC and mobile phones with us.”
Second, they collaborated with complementors (firms that have complementary benefits), especially with furniture manufacturer companies to get access to their resources and technology like dual-core system, wireless receiver, and transmitter. These technologies allowed them to develop locks that perform faster and are more responsive. For this, they relied on previous partnerships. Previous experiences with the same furniture companies drive them to form strategic alliances again with same companies because of intense learning and significantly contribute to new products development. Further, they also formed new partnerships with other furniture companies in Malaysia. Such alliances allowed them to obtain unique and distant knowledge that
236
not available within the company (e.g. keyless lock – finger to unlock your biometric, no key neither card).
Third, for the material and batteries development for electronic and invisible locks they collaborate with local universities and government research institutes. The University of Science
Malaysia and the University of Technology Malaysia provide them material development technology for the “lock tongue” and main parts are made of stainless steel and bottom panel design. Similarly, Malaysia Automotive Robotics and IoT Institute 48 supplied them batteries development technology and technical knowledge (e.g. dry battery power supply, power-saving mode, and low battery consumption). These partnerships lead to intense learning about technologies and expertise that drove them to develop high-quality material and more durable and long-lasting batteries for electronic locks.
7.3.3.4 Summary
Based on Case C analysis, collaboration with external actors significantly contribute to the firm
TC development and building up its brands. Both wider (IOC-breadth) and deeper collaboration
(IOC-depth) are relevant for the company’s success. Wider collaboration allows the firm to obtain new technological knowledge or resources that not available internally; by contrast, deep collaboration provides them with intense technological learning. However, the company focused more on strong collaborators (depth), since focusing on fever partners is easier to managed and less costly compared to maintaining a wider collaboration (as SME have limited resources and capital). Forming new partnerships will cost more, and demand significant time and efforts from managers. Further, as suggested by the interviewees, the company is keen to engage with firms
48 Malaysia Automotive Robotics and IoT Institute is a government research institute under the Ministry of International Trade and Industry tasked to enhance the competitiveness of the Malaysian automotive industry. http://marii.my/
237
and organizations from the domestic market, because those organizations are easier to access compared to overseas companies. Among collaboration actors, customers, suppliers, complementors, universities, and governments research institutes are the most important partners for the company technological learning and TC building.
7.3.4 Case Studies Summary
The case studies analysis explained how successful manufacturing firms in an emerging economy manage to develop TC through IOC with external partners. Case A (EELHT-2) and Case C
(FFSMELMT-15) are Malaysian-owned local companies, while Case B (ESMEHT-5) is a foreign subsidiary and headquartered in Singapore. The three case results strongly supported that both wider (IOC-breadth) and deeper collaboration (IOC-depth) are highly relevant for the firms technological learning and TC building. Wider partnerships allowed them to access the latest information and technology, which were not available within their firms, while deeper collaboration allowed intense learning and access to deeper technological knowledge. However, there are notable differences in their TC development in particular in the way they collaborate or form partnerships to access knowledge and resources from external channels.
There is a significant difference between Case B and C in the way they collaborate with external partners, even though both firms are SMEs. Case B firm put more emphasis on both breadth and depth collaboration with companies from local and oversea countries as a key strategy to access knowledge from the external environment. In contrast, Case C firm put more emphasis on deeper collaboration as the main source of knowledge for TC development. There are several reasons to explain such differences. The nature of MNC company (Case B) actively collaborates with different sources channels to obtain key resources and technologies from different markets. The other possible explanation is that the nature of the business deal with telematics and GPS products
238
required the latest technologies for new or existing products development to stay competitive in the market. The local SME (Case C) considered that wider collaboration is more costly and demands significant efforts from managers than managing the existing narrow partnerships. They were also keen to engage with local firms because it’s easier to access and approachable than companies from overseas. This is mainly due to financial and resources constraints as a local
SME. When the local firms (both Case A and C) do not possess the necessary manufacturing technology, they strategically choose their overseas companies with already formed technological trajectories to obtain core technologies through collaboration.
Case A firm and Case B, both consider collaborations strategies as being important to them.
However, they not keen to collaborate with Malaysian universities and government research institutes because of previous negative outcomes. For them, both institutions are not up to the standards of similar organisations in other neighbouring countries. Those institutions are in the middle of catching up phase world-class universities and research institutes. Contrary, for Case C firm collaborating with government institutes and universities, is key to access technological knowledge and resources. Government institutes and universities have more grants and incentives from the Malaysian government to reduce the gap between industries and public institutions.
Similarly, both institutions keen to work with industry firms to get access to their technologies and resources.
The case studies analysis found that collaboration with customers, suppliers, and consultants are important for TC building in the three firms. Customers or lead users provided valuable information about competitors’ products and conveyed crucial information about users behaviours, which is critical to fulfilling consumers current and future needs. These are more relevant to new technological development or upgrading existing products. Suppliers provide
239
greater learning opportunities on the latest technologies in the market such as new machinery, equipment, component, and software, and can also help in tracking competitors’ movements on new products or services development. Established partnerships with consultants offer solutions to a wide variety of problems, including information on business start-up, marketing and manufacturing activities, new technology, and organization strategy developments. The consultants are more capable of producing fresh and innovative ideas for TC development.
However, the case analysis suggested that Case A and B firms do not prefer to establish any relationships with direct competitors or private R&D. This is because of the risk and complexity of the relationship and the fear that unplanned knowledge leakage might be high. Collaboration with these partners will be risking the company key resources and technologies may leak to competitors firms that could impact their competitive advantage. By contrast, Case C (local SME) considered competitors as an important partner for their TC building, especially with large firms.
The collaboration with large competitor firms allowed them to tap their capital resources, manufacturing processes, and technological expertise.
The other key findings that stand out from the case analysis are the role of complementors, and organizations that offer similar products in different markets and organizations that offer different products in similar markets (similar type of firms, but not direct competitors) are considered as important partners for TC building. Collaboration with complementors (complementary products) creates a win-win situation for both parties and offer more value to the customers together than apart (e.g. locks and furniture products; GPS and cars). Likewise, collaboration with organizations that offer similar products in different markets allowed to obtain similar technologies to them from the overseas and domestic market, while co-operation with organizations that offer different products in similar markets provided information about market conditions, competitors products,
240
and latest technologies. The collaboration with both types of organizations is less risky since the organizations are not direct competitors to them. These enabled them to learn from each other and transfer knowledge between the firms or combine their skills and resources to jointly create new knowledge and competencies. The above three cases provided substantial pieces of evidence to support firms in emerging economies able to build their TC through wider (IOC-breadth) and deeper collaboration (IOC-depth) with external partners.
7.4 Chapter Summary
The interview results confirmed the quantitative analysis of the ten hypotheses. The first shows that both breadth and depth are important collaboration strategies firms use to build their technological capability. The second is that deeper collaboration is more important than wider collaboration in relation to TC development. The third finding, for IOC-depth, is that customers, suppliers and consultants are the most important partners in TC development, while the fourth is that universities and government research institutions are relatively unimportant to manufacturing firms. Fifthly, IOC-depth with competitors and private research institutes is not important in TC building since the risk associated with unplanned knowledge spillovers is high. The other important finding that stands out from this study as a result of the qualitative analysis is that firms actively collaborate with similar types of companies or firms that were operating in the same line of business, but they are not direct competitors to them. Further, collaboration with complementor firms (complementary products) creates a win-win situation for both companies in developing their TC and new products or services that offer to the customers.
The three case studies analysis explained how successfully Malaysian manufacturing firms manage to develop TC through IOC with external organizations. The three case results strongly supported that both wider (IOC-breadth) and deeper collaboration (IOC-depth) are highly relevant
241
for the firms technological learning and TC building. There is a significant difference between
Case B and C in the way they collaborate with external partners, even though both firms are
SMEs. Case B (MNC SME) firm put more emphasis on both breadth and depth collaboration with companies from local and oversea countries, while Case C firm put more emphasis on deeper collaboration as the primary source of knowledge for TC development. This is mainly due to financial and resources constraints as a local SME. Case A and Case B firms not keen to collaborate with Malaysian universities and government research institutes because of previous adverse outcomes. Contrary, for Case C firm collaborating with government institutes and universities, is critical to access technological knowledge and resources. Government institutes and universities have more grants and incentives from the Malaysian government to reduce the gap between industries and public institutions.
The case studies analysis found customers, suppliers, and consultants are essential for TC development. Customers provided valuable information about competitors’ products and conveyed crucial information about users behaviours, which is critical to fulfilling consumers current and future needs. Suppliers provide information on the latest technologies in the market such as new machinery, equipment, component, and software, and can also help in tracking competitors’ movements. Consultants offer solutions for firms problems, including information on business start-up, marketing and manufacturing activities, new technology, and organization strategy developments.
Case A and B firms keen to establish any relationships with direct competitors or private R&D, due to high risk and complexity of the relationship and unplanned knowledge leakage are high. By contrast, Case C (local SME) considered competitors important for TC building, especially with large firms. The large competitor firms allowed them to tap their capital resources, manufacturing
242
processes, and technological expertise. The other key findings the case analysis identified the role of complementors, and organizations that offer similar products in different markets and organizations that offer different products in similar markets (similar type of firms, not direct competitors) are considered as important partners for TC building. Both quantitative and qualitative analysis, including the three cases, provided substantial pieces of evidence to support
Malaysian manufacturing firms able to build their TC through wider (IOC-breadth) and deeper collaboration (IOC-depth) with external partners.
243
CHAPTER 8: DISCUSSION
8.1 Introduction
In Chapter 6, analysis of the MNSI data, the findings of the hypothesis testing and the results of the robustness tests and alternative explanations were presented. Chapter 7 discussed the qualitative findings, including the case studies analysis, are used to answer the research of this thesis. This chapter discusses the overall findings of the research and connects the quantitative and qualitative results with the existing literature and previous studies. Section 8.2 presents the discussion of overall research findings in four sub-sections: 8.2.1 gives an overview of the main findings, 8.2.2 discuss the relationship between IOC and TC building (hypotheses 1-3), 8.2.3 explain the impact of different organizational partners (hypothesis 4-10), and 8.2.4 discuss the differences between large firms and SMEs TC development. The last section summarizes the chapter.
8.2 Discussion of Findings
8.2.1 Overview of the main findings
Both the quantitative and qualitative results of this research, when applied to the main research question and proposed hypotheses, show that IOC for innovation plays a significant role in firms’
TC development in an emerging economy. Several key findings were identified. The first shows that both inter-organizational collaboration breadth (IOC-breadth) and inter-organizational collaboration depth (IOC-depth) are prominent collaboration strategies firms use to build their technological capability (TC) in emerging economies. The second is that IOC-depth is more critical than IOC-breadth concerning to TC development. The third finding, for IOC-depth, is that customers, suppliers and consultants are the most important partners in TC development, while the fourth is that universities and government research institutions are relatively unimportant to
244
manufacturing firms. Fifthly, IOC-depth with competitors and private research institutes is not crucial in TC building since the risk associated with unplanned knowledge spillovers is high.
Other important findings, firms actively collaborate with similar types of companies and firms that operate in the same line of business, which is not direct competitors to develop their products or services. Further, collaboration with complementor firms (complementary products) creates a win-win situation for both companies in developing their TC. Lastly, there are substantial differences between large firms and SMEs in the way develop TC, particular the way they collaborate with external organizations to increased the knowledge base and TC building. Large firms or MNC tend to use both wider and deeper collaboration to increase external knowledge and resources. Big companies have comparatively adequate slack to maintain their pursuit both collaboration strategies concurrently, and they have resources and capital to manage the complexity of collaboration from a wide range of external organizations. Conversely, SMEs relatively experience more resource restrictions, and they may have a compromised between deeper and wider co-operation. The results revealed, SME tends to trade-off wider collaboration
(IOC-breadth) in favour of deeper collaboration (IOC-depth), because concentrating on fever partners is easier to manage and less costly compared to maintaining a broader range of collaboration.
8.2.2 Relationship between IOC and TC
The main research question that this research, seeks to answer whether manufacturing firms in emerging economies able to build their TC through external collaboration? Overall, the quantitative and qualitative findings strongly supported to answer the main research questions.
Both interview and case studies provide an in-deep understanding of the nature of external collaboration and TC development in emerging economies. Both results strongly suggesting that
245
co-operating with specific external organizations is significant for TC building among manufacturing firms in Malaysia.
Together, these findings provided further empirical evidence to support the linkages between IOC for innovation and TC building in emerging economies. These findings are consistent with previous studies, for instance, Lall’s (1993b) belief that stimulation of external collaboration and the progress of organizations to undertaking activities beyond firms’ boundaries is crucial to TC development. The findings also agreed with Bell and Pavitt (1993, 1995) that TC develops as a result of intense co-operation between firms and external partners. The findings from the interviews and case studies analysis further suggested that firms co-operate to acquire new knowledge about production processes. For instance, a General Manager said that “for new or significant improvement product lines or processes, we collaborate with outside partners to develop them. Several of our key items of furniture and locks have been developed and improvised from external engagement with different partners” (FFSMELMT-15). Further, this is confirmed by the argument of evolutionary economic theory, that external search with different organizations allows firms to develop new combinations of knowledge and to follow new technological pathways (Nelson and Winter, 1982; Metcalfe, 1994). By increasing their interaction with external actors, firms can access greater learning opportunities and new knowledge, which can improve TC
(Lamin and Dunlap, 2011; Lee et al., 2001; Temel et al., 2013).
Firms that actively participate in formal organizational collaboration are more capable of transferring valuable external knowledge to enhance their internal capabilities and they are more knowledgeable in managing relationships with co-operating partners (Miozzo et al. 2016b;
Miozzo and Walsh 2006). For example, a Managing Director claimed that “our company extensively collaborates with different external organizations to develop our products and
246
services, especially from technological aspects such as GPS in our flagship multi-constellation and full-featured tracking device” (ESMEHT-5). This supports previous findings that external collaboration strategy within a technological trajectory can significantly influence technological capability building in emerging economies (Lall, 1992; Wignaraja, 1998; Wong, 1999; Hansen and Ockwell, 2014). These results mean that formal IOC with suppliers, customers, competitors, private R&D or consultants, universities and government research institutions are an important source of knowledge for TC building (Lall, 1992, 2000; Kim, 2000; Lamin and Dunlap, 2011;
Belderbos et al., 2011). Lamin and Dunlap’s (2011) results also support the findings of this study and show that both inter-firm and intra-firm co-operation with external actors have a great influence on TC in developing countries.
8.2.1.1 IOC-breadth and TC
One of the key questions this research seeks to answer is to understand the relationship between
IOC-breadth and TC building in emerging economies. This is encapsulated in hypothesis 1 inter- organizational collaboration breadth has a positive association with technological capability building was proposed to guides the study. Overall, the results of the analysis support the proposed hypothesis for both TC input and TC output. This means that when firms collaborate widely with a large number of organizational channels or external partners to obtain knowledge and resources for their TC development. Further, the quantitative results also reveal that IOC- breadth significantly influences R&D activities (TC input) and innovation outcomes (TC output) such as new or significantly improved products or services and processes. As mentioned in the literature review, firms co-operate widely (IOC-breadth) with a large number of organizational channels to access new knowledge and diversify sources of knowledge and resources, which are essential for TC development. In line with this finding, Laursen and Salter (2014) and Cruz-
González et al. (2015) stated that more significant technological innovation (or TC development)
247
primarily comes from the broader range of knowledge search in a variety of technological domains.
This finding is consistent with the number of previous findings. For example, they agree with
Katila (2002) and Katila and Ahuja (2002) that IOC-breadth with numerous external actors within a technological trajectory can influence TC building. Lodh and Battaggion (2014) argue that some technical knowledge is tacit in nature, and IOC-breadth is an effective strategic choice to acquire it from external partners. Access to tacit knowledge from outside of firms is crucial for increasing the breadth of collaboration and exploring new technologies (Laursen, 2012; Miozzo et al., 2016).
According to Katila and Ahuja (2002) and Nelson and Winter (1982), wider collaboration with different partners adds distinctive new variations to existing knowledge and expands an organization’s new product lines, which increased the firms absorptive capacity and TC building.
Further, the interview results and case studies analysis supported the first proposition.
Specifically, interviewees suggest that in technology-based industries, IOC with new actors provides excellent opportunities for firms to access new technological knowledge that is not available within the existing or internal resources pool. For example, a Senior Manager said that “Even though we have key strategic partners, our company is also aggressively engaged with new external partners to obtain the resources and technologies not available internally” (HELHT-8). The above statement is consistent with the findings of Powell et al.
(1996) and Zhang and Baden-Fuller (2010) in the setting of pharmaceutical companies. Their studies assessed biotechnological knowledge and resources from their engagement with universities and new biotech companies. Forming partnerships with new organizations have a strong possibility of new technology development which firms may not obtain from existing partners. The wider collaboration increases the flexibility of firms to identify the right partners
248
that could bring more novelty in technical development. Lall (2000) argues that in emerging economies, different technologies (or TC development) can evolve from a wider range of skills and knowledge by interaction with external partners.
Similarly, the three case studies analysis (Case A, B and C) findings also suggested that wider collaboration allowed them to access the latest information or technology, which were not available within their firms are highly relevant for technological learning and TC building. Based on both quantitative and qualitative results, this research concludes that IOC-breadth is critically important for TC building of firms in emerging economies. This is because IOC-breadth is important for adding distinctive new variations, acquiring new knowledge that does not exist internally and also reduces risk related to technology and market.
8.2.1.2 IOC-depth and TC
Hypothesis 2 examines whether IOC-depth has a positive relationship with TC building. The assumption was tested using logistic regression, and the results reveal a statistically significant relationship for both TC input and TC output. Similarly, the interview results strongly support the positive association between IOC-depth and TC development. Overall, this finding means that when firms collaborate narrowly (IOC-depth), the extent to which they draw deeply on knowledge and resources from familiar organizational channels enables them to build TC. Hamel and
Prahalad (1994) and Kim (1999) also claimed that intense collaboration with a few partners enables firms to access deep knowledge from specialized technological areas, which leads to building core competencies and TC in emerging economies. Another important finding was that
IOC-depth with external organizations, especially among technology-related firms, is crucial in obtaining experience and knowledge relevant to organizational learning, specifically long-term learning, in emerging economies (Kim, 1999; Marcelle, 2004). This further shows that intense
249
learning from co-operating partners is essential to access key technological resources and specialized knowledge that can facilitate the technological learning and TC building at the organizational level in emerging economies (Kim, 1999). According to Kim (1997,1998), deeper collaboration with limited partners increased the firms technological learning and absorptive capacity.
The three case results suggested that deeper collaboration (IOC-depth) are highly relevant for the firms technological learning and TC building. Deeper co-operation with specific partners enhanced their intense learning and access to in-depth technical knowledge. This finding is consistent with previous findings. The recent study by Zhang (2016) suggests that intense collaboration with relevant partners can increase market share and also influence TC development. Likewise, Ferreras-Méndez et al. (2016) study implied that increasing search depth allowed for rapid learning based on the partners’ existing knowledge and expanded the firm’s absorptive capacity. Laursen and Salter (2006) suggested that IOC-depth with existing external partners provides the greater success of TC development.
According to Bell and Pavitt (1993) and Gulati et al. (2009), TC development mainly comes from
IOC-depth, where intense co-operation provides a sizable advantage through understanding the partner’s norms, habits and routines. One of the Managing Directors interviewed said that
“Collaborating with our existing partners gives us more flexibility and effectiveness... Those partners know us well and what are our expectations from the partnership. Experience our company develop our products, processes and new technologies faster” (ESMEHT-5). Likewise,
Eisenhardt and Tabrizi (1995) claimed that learning from existing co-operation partners is important to effectively deal with product development problems and increase the efficiency of process flow by eliminating unnecessary steps. The stock of past collaboration and experience
250
provides the base upon which firms can develop the capabilities and absorptive capacity to cope with new and emerging technologies (Lall, 2000). IOC-depth is therefore essential in drawing deeply on key sources of knowledge from familiar organizational partners within a particular technological or application domain for TC development in emerging economies.
8.2.1.3 IOC and TC (IOC-depth more important then IOC-breadth)
This study also seeks to answer which IOC strategy (IOC-breadth or IOC-depth) is more important for TC building in emerging economies. The third hypothesis IOC-depth has a stronger and positive association with technological capability building compared to IOC-breadth. The quantitative results partially support this hypothesis. The results reveal that IOC-depth has a stronger and positive association with TC building compare to IOC-breadth is statistically significant with TC-output, but is not substantial for TC input. Both interview results and case studies analysis provided more details related to the third proposition.
The interview results strongly suggest that IOC-depth is more important than IOC-breadth for TC development, especially for new or significantly improved products or services and processes development. Even though Malaysian manufacturing firms engage with a larger number of external actors, for their TC development they prefer to collaborate with a few specific organizations (sometime known as strategic partners). For example, a Senior Manager said “Our existing partners provide us with valuable input and flexibility to develop products and processes.
The cost of collaboration with our existing partners is much lower compared to new partners”.
(FCLLMT-12). Another interviewee claimed that “We have mutual trust between us and our strategic partner, which prevents our key knowledge and our latest product development information leaking to competitors” (ESMEHT-5).
251
Similarly, the case studies findings hinted deeper collaboration more critical for them in TC development. The deeper cooperation allowed them to access in-depth knowledge and create a proximal experience, with intense technological learning from collaborating partners. For example, Case C (Malaysian SME) put more emphasis on deeper collaboration as the primary source of knowledge for TC development. The SME considered focusing on fever partners is easier to managed and less costly compared to maintaining a wider collaboration (as SME have limited resources and capital).
The results obtained from both quantitative and qualitative analysis are consistent with a number of previous empirical findings. Lodh and Battaggion (2014) found that intense interaction (IOC- depth) with specific partners is more important in enhancing the learning experience and trust of co-operating partners. According to Bell and Figueiredo (2012) and Hansen and Ockwell (2014),
IOC-depth is the key for manufacturing firms to access knowledge in-depth and build TC from technology-based firms. Likewise, Lall (1992, 2000) argued that often in emerging economies, manufacturing firms’ technological development comes from deeper interaction with external organizations like universities and government research institutes (see also Kumar and
Siddharthan, 1997; Pietrobelli, 1998). However, the findings from this research show that universities and government research institutes are not the most influential co-operation partners.
The interview results also reveal that IOC-breadth is important for TC development from the input perspective, where the activities are related to R&D and knowledge acquisition. This finding means that firms collaborate with a wider range of different partners to improve their existing
R&D activities concerning to TC development. The interview results are consistent with the quantitative findings, suggesting that IOC-breadth is more important than IOC-depth in relation to
R&D activities or investment in TC. As mentioned in the literature review, firms co-operate
252
widely (especially with new partners) to access new information, and diversify sources of knowledge and resources to improve the capacity of internal R&D. Leiponen (2012) argues that
IOC-breadth is essential to access information that increases the strength of the R&D department, allowing firms to develop products or processes that are new to the market and new to the world.
Both quantitative and qualitative results highlight two significant findings. First, IOC-depth is more critical for developing new or significantly improved products, services/processes or new technological development. Secondly, IOC-breadth is important to R&D activities related to TC development. However, the second finding may not applicable for Malaysian SMEs. The case studies analysis highlighted the significant level of differences between Case B (MNC SME) and
C (Malaysian SME) in the way they collaborate with external partners, even though both firms are
SMEs. Malaysian SMEs put more emphasis on deeper collaboration as the primary source of knowledge search from external for their TC development (both R&D activities and new products or technical development). Generally, SMEs experience more resource restrictions, do not have in-house R&D, lack the financial resources and fewer inventive employees. Both sets of results explain the nature of emerging economies and the complexity of national innovation systems in
TC development. As pointed out in the literature, TC building in emerging economies is complex, challenging and not a linear process (e.g. see Lall, 1992; Bell and Pavitt, 1993; Wignaraja, 1998;
Molina-Domene, 2012).
8.2.3 Impact of different organizational partners: IOC-depth and TC
The evolutionary theory stresses that the choice of a suitable IOC partner plays a critical role in the development of TC in emerging economies. The following section discusses the key findings of the association between individual IOC and TC development. The individual, organizational channels: customers and suppliers (vertical co-operation), competitors (horizontal co-operation),
253
consultants and private R&D institutes, and universities and government research institutions and the findings are discussed in detail in the following section.
8.2.2.1 Customers and suppliers (vertical collaboration) and TC
Hypotheses 4 and 5 proposed that IOC-depth with customers and suppliers (vertical co-operation) has a positive association with TC building. The quantitative and qualitative (both interview and case studies) results strongly support these hypotheses, for both TC input and TC output, that is, customers and suppliers are important external partners for TC building. The case studies analysis provided empirical evidence on how the three firms develop their TC through collaborating with customers and suppliers. This vertical co-operation gives more to access to customer-specific knowledge, knowledge of markets and new technologies and other information to develop TC. As mentioned in the literature review, IOC-depth with customers and suppliers (vertical co-operation) is crucial for firms to access information and knowledge about markets, new technologies/innovation and also TC development (e.g. von Hippel, 1988; Tether, 2002; Nieto and
Santamaría, 2007).
Both quantitative and qualitative analysis suggested that collaboration with customers important for firm’s TC development. The result is consistent with that of several previous studies. For example, Gnyawali and Park (2011) conclude that intense collaboration with lead clients or customers can transform a business. IOC-depth with lead clients providing learning opportunities on markets and new customers and technical resources that are not available internally (Lamin and Dunlap, 2011; Chen et al., 2015). Similarly, one interviewee said that “We frequently talk to our strategic customers to develop and improve products or services. Our strategic customers often share their expectations in relation to how we could improve our current products” (SCSMEHT-9). Fritsch and Lukas (2001) claimed that IOC with customers is
254
important to achieve more novel product innovation and the development of complex technologies.
Other interesting findings from the interviews reveal that firms engage in intense collaboration with customers to access their latest product development and technologies and learn about customer expectations and requirements. The findings are aligned with those of von Hippel (1988) and Tether (2002), that co-operating with lead customers is the shortest route to access and get to know competitors’ movement on technology and innovation. Active interaction with customers provides critical information to identify the problems or weaknesses of existing products or processes, and at the same time acquire the tacit knowledge necessary to rebuild or add value to existing products or services (von Hippel, 1988; Chandran et al., 2014; Chen et al., 2015).
Secondly, suppliers are essential for TC building, and the result is consistent with a number of previous findings. The research finding of Tether (2002), argued that Japanese automobile and electronics companies tend to co-operate more with suppliers for innovation processes and technological development. Electronic data interchange (EDI) firms in the USA implemented new
EDI technology after intense collaboration with their manufacturing suppliers (Angeles et al.,
1998). One interviewee claimed that “Our suppliers know about the latest market demand of customers and transactions in the market (market progress). The suppliers have technology and experience related to new products, so we take the opportunity to learn about these new technologies and the method of production” (CLHT-10). Similarly, Khan (2013) showed that the
Suzuki car manufacturer collaborated with suppliers to enhance their TC to meet local requirements and reduce production costs in manufacturing low-cost cars in India.
255
Based on the overall quantitative and qualitative results, IOC-depth with customers and suppliers is clearly an important partnership for TC building for firms in emerging economies. This is because IOC-depth with customers and suppliers allows firms to access information about customer dynamic needs, understand the markets better, and improves the success of new technologies/innovation thereby, creating the shortest route to get to know competitors latest movement on new products development and technologies.
8.2.2.2 Competitors (horizontal collaboration) and TC
Hypothesis 6 proposes that IOC-depth with competitors has a positive relationship with TC building. The statistical results partially support this hypothesis, as IOC-depth with competitors is significantly positively associated with TC output, but not with TC input. The statistical analysis indicates that IOC-depth with rivals (horizontal co-operation) is essential for TC building from an output perspective; however, the interview and case studies results do not support this. This is perhaps because the majority of the senior managers interviewed were not willing to collaborate with their direct competitors to develop their TC, for both R&D activities and products or services and processes development. One interviewee said, “We don’t have kindly relations with our direct competitor’s companies. This is because of our company policy, and we are not interested in taking any risk through dealing with them” (ESMEHT-5).
Both the quantitative and interview results reflect the risk and complexity of competitor relationships. Similarly, the case studies (Case A and B) analysis suggested that firms not prefer to establish any relations with direct competitors or private R&D. This is because of the risk and complexity of the relationship and the fear that unplanned knowledge leakage might be high.
Collaboration with these partners will be risking the company key resources and technologies may leak to competitors firms that could impact their competitive advantage. As reported in the
256
literature review, IOC-depth with competitors is less common for many firms (Hamel et al., 1989;
Tether, 2002; Bell and Figueiredo, 2012). Co-operation with competitors is a risky and complex relationship, and unplanned knowledge spillovers are high when compared with other collaboration partners (Miotti and Sachwald, 2003; Laursen and Salter, 2014), and there is the possibility of anti-competitive behaviour (Tether, 2002: 952).
Overall, the quantitative and interview results are consistent with other studies. For instance,
Bayona et al. (2003) showed that competitor relationships do not seem to be an essential mechanism to develop new products innovation. Miotti and Sachwald (2003) agreed that co- operation with competitors is not statistically significant in sharing innovative products and collaboration with competitors is rare, but although firms may co-operate to reduce R&D costs and the risks associated with innovation. Laursen and Salter (2014) stated that risks are much higher when co-operating for innovation and engaging in capability development with rival firms than with other innovation collaboration partners. Similarly, one interviewee said that “Collaboration with competitors involves high risk, and also our key technologies and product information may leak to them” (SCSMEHT-9).
Likewise, the case analysis (Case C - local SME) considered competitors as an important partner for their TC building, especially with large firms. The collaboration with large competitor firms allowed them to tap their capital resources, manufacturing processes, and technological expertise.
The interview results suggested that SMEs keen to collaborate with competitors to utilize their
R&D facilities and bring down the cost of production of new product development. Aschhoff and
Schmidt (2008) found that co-operation with competitors is positively associated with cost reduction, given the high cost of innovation activities.
257
Furthermore, IOC-depth with competitors in emerging economies is considered riskier, and firms refuse to collaborate with rivals more frequently than in developed countries (e.g. see Lall, 1992;
Rasiah and Chandran, 2009; Lamin and Dunlap, 2011). The findings of Hsu et al. (2009) show relatively few collaborative relationships with competitor firms relatively weak towards innovation and TC development. Bell and Pavitt (1993) and Wignaraja (1998) have similarly pointed out that the nature of emerging economies makes firms avoid engaging with competitors for any technical or technological development.
The other important finding from the interviews and case studies is that manufacturing firms actively collaborate with similar types of companies or organizations that offer similar products in different markets and organizations that offer different products in similar markets (similar type of firms, but not direct competitors), and complementors for their TC development. The majority of the interviewees claimed that they collaborate with similar types of firm to develop their TC, especially when developing new products or services or processes. This co-operation provides them with a different dimension of knowledge and technology, enabling them to build stronger
TC and also improve their marketing-related activities. This is one of the main reasons for manufacturing firms to develop products that are not only new to the market and also new to the world, also known as breakthrough innovation. For example, one interviewee said that, “Our company frequently co-operates with companies in the same line of business; however, those companies are not our competitors. Co-operating with these companies allows us to make a new product and process novelty that increases our market share and also taps into new markets and customers” (FCLLMT-12). Belderbos et al. (2004) also found that firms actively engage with companies that are not direct competitors, for marketing independent or complementary goods leading to strengthening their product market position and new interventions. Co-operating with firms that are not direct competitors is less risky (Belderbos et al., 2004).
258
The case study suggested that collaborating with complementors (complementary products) creates a win-win situation for both parties and offer more value to the customers together than apart (e.g. locks and furniture products; GPS and cars). Guimón (2013) claimed that intense collaboration with a similar type of company provides access to complementary technological knowledge or assets (including patents and tacit knowledge), which is important for commercial and technological success.
Overall, based on the quantitative analysis and interview results, the findings show that manufacturing firms are not interested in forming IOC-depth with competitors when building their
TC. The exception is SMEs, who are willing to engage with competitors to support their R&D activities and bring down the cost of developing new products or TC development. Other important findings, firms formed collaboration with organizations that offer similar products in different markets and organizations that offer different products in similar markets (similar type of firms, but not direct competitors), and complementors for their TC development.
8.2.2.3 Consultants and private R&D institutes and TC
Hypotheses 7 and 8 propose that IOC-depth with consultants and private research institutes has a positive association with TC building. The statistical results revealed that hypothesis 7 is partially supported, IOC-depth with consultants is statistically significant with TC output, however, TC input is not significant. The results mean that consultants are an important external partner for firms’ TC building from an output perspective. The literature suggests that IOC-depth with consultants are increasingly considered as an important partner in accessing information and knowledge for innovation and TC building in emerging economies (Chandran et al., 2014;
Srinivasan, 2014; Chen et al., 2015). IOC-depth with consultants which gives access to specialist
259
knowledge providers is necessary for TC building, especially new products and processes development (Tether and Tajar 2008; Sánchez-González 2014; Srinivasan 2014).
The interview and case studies result strongly support this proposition. The case studies analysis suggested consultants offer solutions for the firm’s problems, including information on business start-up, marketing and manufacturing activities, new technology, and organization strategy developments. For example, one manager claimed that “Consultants are crucial for us because they know the market demand. We have a strong relationship with them because we need their experience and expertise and also knowledge to develop our existing products and develop new product lines” ( FBLLMT-1). Srinivasan (2014) agreed that the primary motive for collaborating with consultants was to access technology and operations-related expertise and also to reduce cost. Relationships with consultants’ are more relevant in the manufacturing sector, as firms often use them in implementing new technology (e.g. installation of new production systems) and they transfer their skills and knowledge to the organization (Lall, 1992; Lamin and Dunlap, 2011).
According Tether and Tajar (2008), the main reasons firms appoint external consultants is to approach problems from a different perspective and also provide new insight into technological innovation and TC building. Our results further support the findings of Hansen and Ockwell
(2014), that specialist consultants are necessary to access technical assistance and specialized knowledge in Malaysia.
Despite the theoretical foundation, hypothesis 8 was not supported in the analysis, indicating that
IOC-depth with private research institutes or commercial laboratories is not statistically significant for either TC input or TC output. Similarly, the interviewees and case studies also claimed that manufacturing firms are not willing to engage with private research institutes to develop their TC, because collaboration is costly and risks unplanned knowledge spillovers. One
260
manager as said that, “Collaborating with private R&D is very risky and complex, and co- operating with them may cause us to lose our key product knowledge and technologies to competitors” (FFSMELMT-15). Further, the managers claimed that collaborating with private research institutes is an expensive partnership, increasing production costs; that is, they were not able to produce the products or services at a competitive price (very high cost per unit), resulting in a low profit margin. Belderbos et al. (2004b) and Aschhoff and Schmidt (2008) similarly stated that R&D expenditure is very high when firms collaborate with private research institutes, especially in technology-related industry and the manufacturing sector.
Overall, the quantitative analysis and qualitative results reveal that IOC-depth with consultants is important for manufacturing firms to develop their TC. On the other hand, IOC-depth with private research institutes is not important partners for firms TC building since the cost of collaboration is high and unplanned knowledge spillovers are more significant.
8.2.2.4 Universities and government research institutions and TC
Hypotheses 9 and 10 proposed that IOC-depth with universities and government research institutions have a positive association with TC building. The quantitative findings partially supported hypothesis 9; IOC-depth with universities is significantly positive with TC input, but with TC output is not significant. This finding means that firms that form IOC-depth with universities enjoy more successful TC development from the input perspective, R&D activities and investment related to technological advancement. On the other hand, the quantitative findings also partially supported hypothesis 10, that IOC-depth with government research institutions is statistically significant with TC output; however, with TC input it is not significant. The findings show that IOC-depth with government or public research institutions is essential for TC building from an output perspective - innovation outputs such as products or services or processes
261
development. The literature considers IOC-depth with universities and government research institutions as critically important to obtain specialist knowledge, scientific/technical information and also new technological frontiers (Tether, 2002; Miotti and Sachwald, 2003; Belderbos et al.,
2004; Rasiah and Chandran, 2009; Temel et al., 2013). It also claims that governments play an important role in boosting research activities in universities and public research institutions for TC development in emerging economies (Rasiah and Chandran, 2009; Lamin and Dunlap, 2011;
Chandran et al., 2014).
The interview results intended to support and clarify the quantitative findings for hypotheses 9 and 10 reveal two sets of findings. First, the majority of managers of the large firm stated that they do not collaborate with universities or government research institutions for any TC development, as they believe that universities lack the requisite practical knowledge and capacity to engage in technological collaboration. Similarly, both Case A firm (large firm) and Case B (MNC SME) not keen to collaborate with Malaysian universities and government research institutes because of previous adverse outcomes, and both institutions are not up to the standards of similar organizations in other neighbouring countries. Those institutions are in the middle of catching up phase world-class universities and research institutes. For example, a Managing Director claims that, “I think Malaysian universities do not reach international standards, compared with the world standards of Singapore’s universities. In terms of research they are still far behind several universities in Asia” (ESMEHT-5). This is consistent with previous studies. For example,
Chandran et al. (2014) found the collaboration between university and industry to be relatively weak; manufacturing firms prefer an intense relationship with customers, suppliers and technical service providers, but not with universities or government research institutions. Iammarino et al.
(2012) found that IOC for innovation with universities had a negative relationship with
262
technological capabilities and competencies. Hsu et al. (2009) further showed weak inter- organizational collaboration with universities and public research institutions.
However, the other set of interviewees (mainly SMEs) declared that co-operating with universities and government research institutions is critically important for TC building. This finding means that SMEs actively engage with universities and government research institutions to improve their
R&D activities and at the same time develop new products or services and processes. The Case C
(Malaysian SME) analysis indicated that collaboration with government institutes and universities are important to access technological knowledge and resources. Government institutes and universities have more grants and incentives from the Malaysian government to reduce the gap between industries and public institutions. Similarly, both institutions keen to work with industry firms to get access to their technologies and resources.
For example, the CEO of one SME said that, “We mainly collaborate with local universities and government research institutions to overcome our knowledge shortage and also access technological facilities to further develop our products with them” (FBLLMT-1). Similarly, the empirical results of Arranz and Arroyabe (2008) showed that firm size has a negative relationship on collaboration with universities and public research institutions, because SMEs have limited resources, and especially technological knowledge makes them co-operate with universities and public research institutions. Further, Chandran et al. (2014) found similar results that SMEs tend to collaborate with universities and government research institutions more than do large firms in
Malaysia. Firms in emerging economies have a low level of internal R&D competence, making universities and public research institutions a critical source of external knowledge to enhance their understanding of new scientific developments (Chen et al., 2015).
263
Overall, based on the quantitative analysis and interview results, IOC-depth with universities and government research institutions is relatively weak among manufacturing firms in relation to TC development. Only SMEs are keen to collaborate with them in order to undertake larger innovation projects and to overcome the limitations of their own R&D, and also to benefited from
Malaysian government grants and incentives to collaborate with government institutes and universities.
8.2.3 Large firms and SMEs TC development
The other significant findings that emerged from interview results and case studies analysis, there are substantial differences between large firms and SMEs in Malaysia in the way develop TC. In particular, the way they collaborate with external organizations to increased the knowledge base and TC building. The results suggested that large firms or MNC tend to use both breadth and depth collaboration to increase external knowledge and resources. Large firms or MNC have capacity and resources to the pursuit both wider and deeper collaboration simultaneously, and they have experienced managing the complexity of external search from a wide range of knowledge sources, which often lack by SMEs (Hsu et al. 2013; Vrgovic et al. 2012; Chandran et al. 2014). By contrast, SMEs relatively experience more resource restrictions, and they may have a compromised between deeper and wider co-operation. These findings are consistent with previous studies, for instance, Vrgovic et al. (2012) argued that SMEs in emerging economies tend to faced significant resource constraints compared to larger firms, such as lack of capital, skill employees, not possessed with own R&D and others. Increasing external collaboration networks requires more complex coordination if optimal advantages are to be provided (Diez
2000; Nieto & Santamaria 2010). Such collaborations are challenging to manage, and require considerable managerial and financial resources, which are accessible to large firms (Chandran et al. 2014; Dooley et al. 2017), but are unavailable to most SMEs, especially in emerging
264
economies. According to Guo et al. (2015) in a resource-constrained context, SMEs in emerging economies tend to trade-off between search depth and search breadth for their technological development and innovation activities.
SMEs generally encounter more uncertainties and difficulties to build capability, and the collaboration is identified to be a complementary response to insecurity arising from development and use of new technologies, while reducing uncertainties in TC development (Diez 2000; Arranz and Arroyabe 2008). Studies from the emerging economies pointed out that innovation collaboration with external organizations are becoming more and more crucial for SMEs to promote their TC (e.g. see Nieto & Santamaria 2010; Vrgovic et al. 2012; Chandran et al. 2014).
Dooley et al. (2017) research result showed that SMEs have a significant impact on their innovativeness and technical development when collaborating with specific partners. SMEs are found to maintain few external interactions in their innovation activities (Kaminski et al. 2008;
Arranz and Arroyabe 2008), being careful about selecting actors, because they have narrowed possibilities to fail (Narula 2004; Nieto & Santamaria 2010). Particularly in emerging economies,
SMEs rarely practice collaboration, which results in the small number of combined business processes between two or more firms (Diez 2000; Salman 2004).
8.3 Chapter Summary
Both quantitative and qualitative findings provide in-depth solutions to answer the research question and proposed hypotheses. Overall, both quantitative and interview results support proposals that IOC for innovation plays a major role in firms TC development in emerging economies. The case studies analysis showed how firms are implementing collaborations when dealing with the issue of building technological capability via external partnerships. Both quantitative and qualitative findings are connected with the existing literature and previous studies
265
to understand the nature of those relationships in the context of emerging economies. The previous literature and prior studies strongly support the final research results.
Overall, several key findings were identified in this study. First, both IOC-breadth and IOC-depth are important collaboration strategies in building TC in emerging economies. Second, IOC-depth is more important than IOC-breadth in relation to TC development. Third, IOC-depth with customers, suppliers and consultants are the most important partners for TC development. Fourth,
IOC-depth with universities and government research institutions is relatively weak among manufacturing firms. Fifth, IOC-depth with competitors and private research institutes are not important partners for TC building since the risk associated with high and unplanned knowledge spillovers are greater.
Sixth, collaboration with similar types of companies or organizations that offer similar products in different markets and organizations that offer different products in similar markets (similar type of firms, but they are not direct competitors), and complementors allowed firms the develop their TC development. Collaborating with similar types of firms provides a different dimension of knowledge and technology, which enables them to build stronger TC and also improve their marketing-related activities. Complementor firms (complementary products) offered firms a win- win situation for both companies in developing their TC. Lastly, the other important finding that stands out from this research that there are substantial differences between large firms and SMEs in the way develop TC, especially in the way they collaborate for TC building. For large firms or
MNC both breadth and depth collaboration consider important to increase external knowledge and resources for TC development. Conversely, SMEs relatively experience more resource restrictions, and they may have to compromise between deeper and wider co-operation. The
266
interviews results reveal, SME tends to trade-off wider collaboration (IOC-breadth) in favour of deeper collaboration (IOC-depth) for their TC development.
267
CHAPTER 9: CONTRIBUTIONS, LIMITATIONS AND DIRECTIONS FOR FUTURE RESEARCH
9.1 Introduction
The previous chapter discussed the quantitative and qualitative research findings and connected them with existing literature and previous studies. The main aim of this thesis is to examine to what extent formal inter-organizational collaboration affects technological capability building in emerging economies. In particular, it analyses two collaboration strategies: IOC-breadth and IOC- depth, and their relationship with TC building; it also specifically investigates the influence of
IOC-depth on TC development with suppliers, customers, competitors, consultants, private R&D, universities, and government research institutions. The results have suggested that IOC for innovation plays an important role in firm’s TC building in emerging economies. Overall, several interesting findings were identified.
First, the results show that in Malaysia, both IOC-breadth and IOC-depth are important collaboration strategies that firms use to build their TC. Such a result may also hold for other emerging economies. Second, IOC-depth is more critical than IOC-breadth. Thus, firms develop their TC by building deeper collaborations with few partners rather than with many. A third result suggests that not only is IOC depth generally essential, but it is driven by deeper relationships with customers, suppliers, and consultants. IOC-depth with universities and government research institutions is relatively weak, and with competitors and private research institutes is inadvisable because the risks associated with unplanned knowledge spillovers are more significant. Fourth, collaboration with similar types of companies or organizations that offer similar products in different markets or firms that offer different products in similar markets (not direct competitors), and complementors allowed firms the develop their TC. Thus, SMEs tend to trade-off wider
268
collaboration (IOC-breadth) in favour of deeper collaboration (IOC-depth) for their TC development.
The rest of the chapter is organized as follows. Section 9.2 presents the key contributions and theoretical insights to the field of study. Section 9.3 discusses the limitations that emerged from the analysis. The last section outlines some avenues for future research, which may extend knowledge in the emerging economies context and followed by conclusion.
9.2 Contributions to the Field of Study
Based on the gaps identified in the literature, the findings of this thesis make a number of theoretical, empirical, practical, and management and policy contributions to related areas. They refine our view of TC development through IOC with external partners in emerging economies.
The following section discussed the key contributions of the thesis.
9.2.1 Theoretical contributions
First, the theoretical framework in Chapter 3 represents an original conceptualization to the study of technological capability development in emerging countries. It considers the relationship and impact of Inter-Organisational Collaboration(IOC) on Technological Capability (TC). There seems to be very little interaction between these two streams of literature. Thus this research contributes to evolutionary economics by linking these literatures from an emerging economies context. The literature on TC building in emerging economies pays insufficient attention to the evolutionary theory of firm-level external technological learning, especially learning by interacting or collaboration with external partners (Kim, 1997; Bell and Figueiredo, 2012; Hansen and Ockwell, 2014). The evolutionary approach to technical change considers TC as the outcome of in-house technological competences and complex interactions among individuals, firms, and
269
organizations within a specific socio-economic and institutional environment. In addition, an evolutionary perspective on organizational interaction with external partners mainly deals with issues relating to firms in developed or industrialized countries (see e.g. Laursen and Salter, 2006;
Nieto and Santamaría, 2007; Iammarino et al., 2012; Miozzo et al., 2016). Although investment plays a direct and significant role in TC building, interacting with other organizations is crucial for firms to build TC in emerging economies. Therefore, this thesis contributes to the theoretical development in the field of evolutionary thinking about development by linking the two streams of literature and showing that firms in emerging economies can develop their TC through collaboration with external partners or organizations.
Second, the study applies this new approach to understanding TC building to Malaysia. By building on an evolutionary perspective focusing on IOC and the importance of different external partners, the thesis uses innovation survey firm-level data and qualitative approach (both interviews and case studies). There is a scarcity of analytical frameworks from the evolutionary perspective, explaining how and what types of external collaboration (intensity of collaboration: breadth and depth) are essential for TC building in emerging economies context. In the context of
Malaysia, the study provides evidence-based perspectives on how IOC-breadth and IOC-depth can, via different channels, drive the development of TC in emerging economies, issues that are pointed out in evolutionary theory. This expands the understanding of the evolutionary theory on
TC development that related intensity of collaboration (also known as linkage capabilities) with external partners. Further, the findings also suggest that there are substantial differences between large firms and SMEs in Malaysia in the way they develop TC, especially how they collaborate with external organizations to build their capability. Large firms and MNCs tend to use both wider and deeper collaboration to increase external knowledge and resources. By contrast, SME tends to trade-off wider collaboration (IOC-breadth) in favour of deeper collaboration (IOC-depth). This
270
thesis contributes to understanding the nature of TC development in emerging economies and the complexity of national innovation systems.
9.2.2 Empirical contributions
This thesis is one of the first studies to provide an in-depth analysis of the impact of IOC on TC building in manufacturing firms in an emerging economy (Malaysia), based both on a large dataset Malaysian National Innovation Survey (MNIS-6), and qualitative method (interviews and case studies analysis). It analyses in detail the relationship between IOC-breadth and IOC-depth and TC building using a sequential mixed-method approach that combines logistic regression methods with interviews and case study analysis.
Very few studies have previously examined this relationship between TC development and IOC with external partners (wider and deeper collaboration). The predominant claim in the literature is that TC building in emerging economies is not clearly understood (Lall, 1992; Wignaraja, 1998;
Lamin and Dunlap, 2011; Molina-Domene, 2012). The current research contributes to closing the gap in the empirical literature using the research framework developed to link IOC and TC.
Newly industrializing countries such as Singapore, Taiwan, Hong-Kong, and South Korea have built up their TC through IOC, a major factor in their rapid export growth and technological upgrading (Hobday, 1994, 1995; Pietrobelli, 1998; Wignaraja, 2001; Tsai, 2004).
The mixed-method analysis (innovation survey data and qualitative approach) used in this study provides an in-depth understanding of how manufacturing firms build their TC via external collaboration with different partners with both breadth and depth collaboration strategies. Using both strategies, manufacturing firms are able to develop their technological capability, although
271
stronger technological capability development only comes from deep interaction with particular partners.
The research results help to understand the nature of larger firms and SMEs TC development in emerging economies. There are substantial differences between large firms and SMEs in the way they collaborate with external partners for their TC building. For large firms and MNCs, both breadth and depth collaboration are important, since they have resources and capital to manage the complexity of such collaborations. SMEs experience more resource constraints and have to compromise between deeper and wider collaboration. The overall findings revealed that SMEs tend to trade-off wider collaboration (IOC-breadth) in favour of deeper collaboration (IOC-depth) for TC development. This provides a clearer picture of how SMEs in emerging economies can upgrade their TC and competencies through external partners.
9.2.3 Practical contributions
This research provides practitioners with an understanding of the importance of inter- organizational collaboration (breadth and depth) in developing technological capability in emerging economies with a specific focus on the manufacturing sector. The nature of IOC in emerging economies is perhaps equally as complex and dynamic as in well-developed countries.
The differences and problems associated with collaboration are discussed in the literature on national innovation systems. In developed countries, innovation systems are characterized by strong knowledge-exploitation and exploration subsystems, and a high degree of interaction between organizations and robust institutional frameworks (Freeman, 1987; Lundvall, 1992). On the other hand, in emerging economies, innovation systems are usually characterized by a deficient socio-economic infrastructure, limited innovative capabilities, weak interactions between different organizations in the system, and inadequate institutional frameworks supporting
272
institutional settings (Arocena and Sutz, 2000; Cassiolato et al., 2003; Lundvall et al., 2009).
Therefore, this research provides important management implications to researchers and practitioners in emerging economies to focus more on external collaboration (both wider and deeper collaboration) for their TC development.
The IOC-breadth adds a new way of searching and improving the possibilities of identifying new sources of information or knowledge that is not available internally and adds distinctive new variations to the existing knowledge base. Further, wider collaboration is useful when a firm is exploring new technology and does not necessarily wish to enter a flexible form of relationship because of the risk and uncertainty of the outcome of the technology’s development. On the other hand, IOC-depth (deeper collaboration) with few or limited partners allowed firms to access in- depth knowledge from a few specialized technological areas and increase learning intensity from collaborating partners. Deeper collaboration provides a sizable advantage in understanding the partner’s norms, habits, and routines. Previous collaborations and experience provide the base on which firms can develop the capabilities to cope with new technologies. The case study analysis suggested that large firms or MNCs tend to use both breadth and depth collaboration to increase external knowledge and resources. Large firms and MNCs have the capacity and resources to pursue broader and deeper collaboration simultaneously, and they have experience in managing the complexity of external search from a wide range of knowledge sources, which often lack by
SMEs.
The other contribution, the finding suggested that deeper collaboration is more important than wider collaboration for TC development in emerging economies. This indicated that researchers and practitioners could focus more on deeper collaboration for their TC development. Deeper collaboration has a significant impact on the firm’s absorptive capacity through learning by
273
interacting (intense technological learning) with external actors. The close relationship with few partners and previous experience is key for an increased firm’s absorptive capacity and TC development in emerging economies.
Similarly, this contribution will be applicable to SMEs in other emerging economies. In a resource-constrained context, SMEs in emerging economies tend to trade-off wider collaboration in favour of deeper collaboration. For SMEs wider collaboration is more costly and demands significant efforts and time from managers to manage the wide range of external partners.
Therefore, SMEs should focus more on depth collaboration, since concentrating on fewer partners is easier to manage and less costly compared to maintaining a broader range of collaboration with external organizations. Therefore, this finding provides a significant contribution to researchers and encourages practitioners to put emphasis on IOC-depth for a better outcome in technological development in emerging economies, especially for SMEs. This sample case can also be applied to other emerging economies and developing countries. This study makes an important contribution to the industrial development of firms in emerging economies.
9.2.4 Management and policy contributions
From a management and policy perspective, this thesis helps managers and policymakers to understand the importance of IOC and two collaboration strategies (IOC-breadth and IOC-depth) for TC development. The finding can be used to inform managers to improve their ability to compete with firms, they need to build technological capabilities. While this can be done by directly investing in technology, the research has shown that it can be achieved by forming collaborations with other firms and organizations. A key challenge in formulating a collaboration strategy is identifying potential partners. It does not seem to matter for large firms, given that they have less immediate resource constraints. However, for SMEs, the situation is quite different.
274
Managers need to invest in innovation carefully. One way to do this is to carefully select a narrow range of collaboration partners and to develop deeper relationships with them.
From a development policy perspective, the thesis has underpinned the importance of IOC. Based on the research findings, Malaysian policymakers should also encourage technological development through firm collaboration. Therefore, they should develop policies to guide and incentivize firms to collaborate with external organizations if they want firms to develop the TC of Malaysian firms, as both broad and deep collaborations are effective ways to achieve technological advancements.
Recommendations for Malaysian innovation policy and National Innovation System were discussed in Chapter 5. In the broader perspective, the government recognizes that more concentration on STI policies and initiatives are needed to reduce the complexity of a frequently changing system. This is necessary to overcome the issues of the multiplicity of priority-setting whereby the focus is on target areas consistent with state plans. This would also increase stability and flexibility in the overall coordination and follow-up on STI projects related to TC.
9.3 Limitations of the Research
Although the present study has produced a number of interesting findings and contributions to knowledge, in investigating the relationship between IOC and TC building in emerging economies.
This research, like all other empirical studies it is not without its limitations. It is important to recognize these limitations and to counter them by suggestions for future research.
First, the current study used a relatively small sample size (445 firms) from the manufacturing sector, which could limit the overall accuracy of the results. For example, CIS studies use data
275
samples of over a few thousand firms for analysis (e.g. see Laursen and Salter, 2014; Iammarino et al., 2012). The small sample size also constrains the robustness of the statistical models developed in this study.
Secondly, the innovation survey is criticized for using qualitative data, such as dichotomous or binary variables (e.g. yes/no questions), categorical data (scoring the importance of innovation), and unordered categorical or nominal variables (e.g. objectives and effects of innovation) (Young,
1996; Smith, 2004). Qualitative data tends to provide less factual information than quantitative data (e.g. sales or turnover of the firm in percentages like 10% or 25%). For example, binary scale survey data allows accessing more information and depth of knowledge from the participants, which enrich the overall research results.
Thirdly, another limitation of innovation survey data is its cross-sectional nature, since the indicators are either similar or because of overlapping sample periods (Mairesse and Mohnen,
2010: 1138; Leiponen and Helfat, 2010). This leads to econometric problems such as multicollinearity, simultaneity bias or endogeneity in data analysis (e.g. Tether, 2002; Cassiman and Veugelers, 2002; Veugelers and Cassiman, 2005). The cross-sectional nature of innovation surveys could be one of the reasons why the results of the analysis of variables (dependent or explanatory) are highly significant or correlated (Smith, 2004; Mairesse and Mohnen, 2010).
Another potential weakness is that these mixed measurement scales handle endogeneity issues with difficulty (Smith, 2004). The time spans of innovation surveys are too short (Mairesse and
Mohnen, 2008; Crespi and Maffioli, 2014); MNSI innovate`ion data is collected based on the 3- year intervals, which are too small for firms to realize the effects of some of their innovation outcomes like new products or processes, and TC building.
276
Lastly, the MNSI was adopted from UK CIS using the perspective of developed countries. Even though the survey is widely used by other emerging economies and developing countries, but there are problems with fit into the nature of those countries. Some of the elements and questionnaires do not fit well in emerging economies or developing countries contexts. Further, the MNSI was not designed to address the specific research questions or problems of this study, so the use of MNSI-6 data may not reach the standards of specifically designed questionnaires.
The above problems and limitations are related to the quantitative approach. In order to deal with these problems, this research has undertaken three alternative models to robust the research findings. The estimated models using an alternative measure of our (1) dependent variable and (2) independent variable, and (3) used a probit model as an alternative to the logit model, following the example of Laursen and Salter (2014) used a similar method for robustness test.
Given the problems mentioned above, this thesis also undertook a qualitative approach with interviews and three case studies to overcome the limitations. The interview results validated the quantitative findings from Chapter 6 and also provide an in-deep understanding of research questions. Besides, the three case studies' findings showed how manufacturing firms are implementing collaborations with external organizations to build their TC. The mixed-method approach helped overcome some of the identified weaknesses and limitations in addressing the research questions and objectives of this thesis.
277
9.4 Directions for Future Research
There are several suggestions for future research. First, future studies could investigate further details of TC, underlining institutional mechanisms that shape these capabilities in emerging economies. It is also important to explore other factors that drive the development of TC and competencies of firms in both developing and emerging economies for industrial development.
The second recommendation for future work is to undertake a more comprehensive quantitative study (with own-design questionnaires) to further explore the nature of TC development in emerging economies. This would allow the inclusion of more questions to capture both variables,
IOC and TC, and also identify the challenges that firms face in building TC.
A further study, exploring the cross-national comparative basis and incorporating both the survey data and interview dimensions used here, is recommended. A cross-national comparative study could provide a wider spectrum of different countries’ TC development and an in-depth understanding of the socio-cultural, economic and political contexts of emerging economies.
Lastly, studies adopting innovation survey data largely focus on manufacturing firms. Future studies may replicate the current research focusing on service firms, especially knowledge- intensive business services (KIBS), enrich knowledge contribution in the field of study. Not only are TC important for emerging economy firms’ competitive advantage and industrial development, but other organizational capabilities such as marketing are equally important. For example, both technological and marketing capabilities play an important role in the industrial development and rapid economic growth of the newly industrializing countries of South East Asia
(Hobday, 1994, 1995; Wignaraja, 2001). Further studies should explore the relationship between marketing capability and IOC from an emerging or developing countries’ perspective.
278
9.5 Conclusion
The primary objective of this research is to analyze how firms’ in emerging economies are able to develop their TC through collaboration with external organizations? Does the intensity of collaboration and collaborating partners matter for TC development in emerging economies? TC development in emerging economies recognized as one of the most critical resources for industrial development. It provides a sustainable competitive advantage to survival in the global market and to compete with international companies. The economic performance of Newly Industrializing
Countries (NICs) - four Asian Tigers, Hong Kong, Singapore, South Korea, and Taiwan have developed relatively stable technological capabilities, which is the key determinant for their rapid export growth and economic development. Malaysia is also known as the new Asian tiger
(second-tier newly industrializing countries) along with Indonesia, Thailand, and the Philippines.
In order to meet the research objective and answer the research questions, this thesis employed a mixed-method approach. Both quantitative and qualitative findings provide in-depth solutions to answer the research question and proposed hypotheses. The overall research findings suggested that formal inter-organizational collaboration (both breadth and depth) affects technological capability building in emerging economies. The other significant result is that, deeper collaboration is more important than wider collaboration for TC development. Cooperation with similar types of companies or organizations (not direct competitors) and complementors allowed firms to develop their TC. Lastly, the other important finding showed there are substantial differences between large firms and SMEs in the way they develop TC, especially in the way they collaborate for TC building.
279
iREFERENCES
Ahuja, G., 2000. Collaboration networks, structural holes and innovation: a longitudinal study. Administrative Science Quarterly, 45: 425–455.
AES, 2018. Annual Economic Survey 2018.
AIM 2011. National innovation strategy: innovating Malaysia. Agensi Inovasi Malaysia, Putrajaya.
AIM 2013. AIM Annual Report 2013, Agensi Inovasi Malaysia, Putrajaya.
AIM 2014. AIM Annual Report 2014, Agensi Inovasi Malaysia, Putrajaya.
Allison, P, D., 1999. Logistic regression using the SAS system: Theory and application. Cary, NC: SAS Institute Inc.
Alvarez, H., Iske, P., 2015. Internal Capabilities and External Knowledge Sourcing for Product Innovation in LMT SMEs. Journal of Innovation Management, 3(2): 55-70.
Amit. R., & Schoemaker. P., 1993. Strategic Assets and Organizational Rent. Strategic Management Journal, 14(1): 33–46.
Angeles, R., Nath, R., Hendon, D. W., 1998. An empirical investigation of the level of electronic data interchange (EDI) implementation and its ability to predict EDI system success measures and EDI implementation factors”. International Journal of Physical Distribution and Logistics Management, 28 (9/10): 773-793.
Arranz. N., & Arroyabe. J. C. F., 2008. The choice of partners in R&D cooperation: An empirical analysis of Spanish firms. Technovation, 28: 88-100.
Arocena, R., & Sutz, J., 2000. Looking at national systems of innovation from the south. Industry and Innovation, 7(1): 55–75.
Arocena, R., & Sutz, J., 2001. Changing knowledge production and Latin American universities. Research Policy, 30 (8): 1221–1234.
Archibugi, D., Pianta, M., 1996. Measuring technological change though patents and innovation surveys. Technovation, 16(9): 451-468.
Arranz, N, Y., Arroyabe, J, C., 2008. The choice of partners in R&D cooperation: An empirical analysis of Spanish firms. Technovation, 28(1): 88-100.
Aschhoff, B., & Schmidt, T., 2008. Empirical evidence on the success of R&D cooperation-Happy together? Review of Industrial Organization, 33(1): 41-62.
ASM 2015. Science Outlook: Action Towards Vision, Research & Policy, Recommendations Document, Academy of Sciences Malaysia.
Atkinson, A. B., Stiglitz J. E., 1969. A new view of technological change. The Economic Journal, 79 (4): 573-578.
Basu Das, S., & P.O., Lee 2014. Malaysia’s Socio-Economic Transformation, Institute for Southeast Asian Studies, Singapore.
Bayona, C., García-Marco, T., Huerta, E., 2001. Firm’s motivations for cooperative R&D: an empirical analysis of Spanish firms. Research Policy, 30: 1289–1307.
Bayona, C., García-Marco, T., Huerta, E., 2003. ¿Cooperar en I+D? Con quién y para qué. Revista de Economía Aplicada, 31(11): 103–134.
Belderbos, R., Carree, M, A., Diederen, B., Lokshin, B., & Veugelers, R., 2004. Heterogeneity in R&D co-operation strategies. International Journal of Industrial Organization, 22(8-9): 1237–63.
280
Belderbos, R., Carree, M, A., & Lokshin, B., 2004b. Cooperative R&D and firm performance. Research Policy, 33(10): 1477-1492.
Belderbos, R. A., Gilsing, V., Jacob, J., 2011. Technology alliances in emerging economies: persistence and interrelation in European firms' alliance formation. (UNU MERIT Working Paper Series; No. 026). Maastricht: UNU-MERIT, Maastricht Economic and Social Research and Training Centre on Innovation and Technology.
Bell, M., Pavitt, K., 1993. Technological accumulation and industrial growth: contrasts between developed and developing countries. Industrial and corporate change 2 (1): 157-211.
Bell, M., Pavitt, K., 1992. Technological accumulation and industrial growth: contrasts between developed and developing countries. Industrial and Corporate Change, 2: 157– 210.
Bell, M., & Pavitt, K., 1995. The development of technological capabilities, in: Haque, I.U. (Ed.), Trade, technology and international competitiveness. The World Bank, Washington.
Bell, M., Pavitt, K., 1997. Technological accumulation and industrial growth: contrasts between developed and developing countries, in Archibugi, D., and Michie, J. (Eds), Technology, globalisation and economic performance. Cambridge University Press, 83-137.
Bell, R. M., Albu, M., 1999. Knowledge systems and technological dynamism in industrial clusters in developing countries, World Development, 27: 1715–1734.
Bell, M., & Figueiredo, P., 2012. Innovation capability building and learning mechanisms in latecomer firms: recent empirical contributions and implications for research. Canadian Journal of Development Studies 33 (1): 14-40.
Bessant, J., & Rush, H., 1995. Building bridges for innovation: the role of consultants in technology transfer. Research Policy, 24: 97–114.
Bodas-Freitas, I. M., Fontana, R. & Adams, P., 2013. Strategic orientations, marketing capabilities and innovation: an empirical investigation, Druid Summer Conference, DRUID Society, Barcelona, Spain.
Bogliacino, F., Perani, G., Pianta, M., Supino, S., 2012. Innovation and Development: The Evidence From Innovation Surveys, Latin American Business Review, 13(3): 219-261. DOI: 10.1080/10978526.2012.730023.
Braun, V., Clarke, V., 2006. Using thematic analysis in psychology in Qualitative Research in Psychology, 3: 77-101.
Bruce, M., Morris, B., 1998. In house, out-sourced or a mixed approach to design. In: Bruce M, Jevnaker B (eds) Management of design alliances: sustaining competitive advantage. Wiley, Chichester.
Cassiman, B., & Veugelers, R., 2002. R&D cooperation and spillovers: Some empirical evidence from Belgium. American Economic Review, 92(4), pp.1169-1184.
Cassiolato, J, E., Lastres, H, M, M., & Maciel, M, L., 2003. Systems of innovation and development: evidence from Brazil. Edward Elgar Publishing.
Chandran, V.G.R., 2010. R&D commercialization challenges for developing countries: The case of Malaysia. Tech Monitor, Nov-Dec, pp. 25-30.
Chandran, V.G.R. & Rasiah, R., 2013. Firm size, technological capability, exports and economic performance: the case of electronics industry in Malaysia. Journal of Business Economics and Management, 14(4), pp.741–757.
Chandran, V.G.R., Sundram, V.P.K. & Santhidran, S., 2014. Innovation systems in Malaysia: a perspective of university-industry R&D collaboration. AI and Society, 29, pp.435–444.
Chee, Peng Lim. 1982. From import-substitution to export promotion: A study of changes in Malaysia’s industrial policy. In Trade and industrial policies of Asian countries, ed. K. Yoneda. Tokyo: Institute of Developing Economies.
281
Chen, H., Li, Y., Liu, Y., 2015. Dual capabilities and organizational learning in new product market performance. Industrial Marketing Management, 46: 204–213.
Chung, S., Kim, G. M., 2003. Performance effects of partnership between manufacturers and suppliers for new product development: the supplier's standpoint. Research Policy, 32(4): 587-603.
Chuang, S., 2016. Electronics and Global Value Chains. Routledge Handbook of Industry and Development ed. John Weiss and Michael Tribe (Abingdon: Routledge, 29 Sep 2015), accessed 24 Dec 2018. Routledge Handbooks Online.
Christou, G., 2006. The impact of the internet on travel agencies in Cyprus. How has this impact affected the Cypriots' consumer behaviour? Msc dissertation, University of East Anglia.
Chesbrough, H., 2003. Open Innovation: the new imperative for creating and profiting from technology. Harvard Business School Press, Boston.
Chesbrough, H., 2006. Open innovation: a new paradigm for understanding industrial innovation. In: Chesbrough, H., Vanhaverbeke, W., West, J. (Eds.), Open Innovation: Researching a New Paradigm. Oxford University Press, Oxford, pp. 1–12.
Cohen, W.M., Levinthal, D.A., 1990. Absorptive capacity: a new perspective of learning and innovation’, Administrative Science Quarterly, 35(1): 128–152.
Colombo, G. M., Piva, E., Rossi-Lamastra, C., 2014. Open innovation and within-industry diversification in small and medium enterprises: The case of open source software firms. Research Policy, 43: 891–902.
Coombs, J, E., Bierly, P, E., 2006. Measuring technological capability and performance. R&D Management, 36: 421- 438.
Crespi, G, A., Maffioli, A., 2014. Design and Evaluation of Fiscal Incentives for Business Innovation in Latin America: Lessons Learned After 20 years of Experimentation. In: Crespi G., Dutrénit G. (eds) Science, Technology and Innovation Policies for Development. Springer, Cham.
Crespi, G., & Zuniga, P., 2012. Innovation and productivity: evidence from Six Latin American Countries', in: World Development, 40 (2), pp.273–290.
Creswell, J., 1998. Qualitative inquiry and research design: Choosing among five traditions. Thousand Oaks, CA: SAGE Publications.
Cresswell, J., Maitta, R. C., 2002. Qualitative research. In: Miller, D. and Salkind N. J. eds. Handbook of research design and social measurement USA: SAGE Publications, 143-97.
Creswell, J., 2003. Research design: Qualitative, quantitative and mixed methods approaches (2nd ed.). Thousand Oaks, CA: SAGE Publications.
Creswell, J., Plano Clark, V., 2007. Designing and Conducting Mixed methods research. California: SAGE Publications Inc.
Creswell, J. W. (2007). Qualitative inquiry and research design: choosing among five approaches.
Creswell, J.W. & Vicki L Plano Clark, 2011. Designing and conducting mixed methods research.
Creswell, J.W., 2013. Research design (International student edition): qualitative, quantitative, and mixed methods approaches.
Daft, R. L., Weick, K. E., 1984. Toward a Model of Organizations as Interpretation Systems. Academy of Management Review, 9: 284-295. http://dx.doi.org/10.5465/AMR.1984.4277657
Day, N., & A. Muhammad 2011. Malaysia: The Atlas of Islamic-World Science and Innovation: Country Case Study No. 1, British Council.
282
Dantas, E.; Giuliani, E.; Marin, A., 2008. The Persistence of ‘Capabilities’ as a Central Issue in Industrialization Strategies: How They Relate to MNC Spillovers, Industrial Clusters and Knowledge Networks, Asian Journal of Technology Innovation, 15(2): 19-43.
De Faria, P., Lima, F., Santos, R., 2010. Cooperation in innovation activities: the importance of partners. Research Policy, 39: 1082–1092.
De Faria, P., Dolfsma, W., 2011. Innovation Failure: Which Capabilities Prevent It? Paper to be presented at the DRUID 2011 on INNOVATION, STRATEGY, and STRUCTURE - Organizations, Institutions, Systems and Regions at Copenhagen Business School, Denmark, June 15-17, 2011.
Denzin, Norman K. (1973). The research act: A theoretical introduction to sociological methods. New Jersey: Transaction Publishers.
Diez, J. D., 2000. Innovative networks in manufacturing: Some empirical evidence from the metropolitan area of Barcelona, Technovation 20(3), 139–150.
Dittrich, K., Duysters, G., De Man, A., 2007. Strategic repositioning by means of alliance networks: The case of IBM. Research Policy, 36; 1496–1511.
Dosi, G., 1988. Sources, procedures, and micro economic effects of innovation. Journal of Economic Literature, 26: 1120–71.
Dodgson, M., 1993. Learning, trust, and technological collaboration. Human Relations, 46(1): 77–95.
Dosi. G., R. Nelson., & S. G. Winter., 2000. The nature and dynamic of organizational capabilities. Oxford/New York: Oxford University Press.
Dosi. G., Fagiolo. G., & Roventini. A., 2008. The microfoundations of business cycles: An evolutionary, multi-agent model. Journal of Evolutionary Economics, 18: 413-432.
Duffy, M.E. (1987) Methodological Triangulation: A vehicle for Merging Qualitative and Quantitive Research Methods. Journal of Nursing Scholarship, 19(3): 130-133.
Duysters, G., Lokshin, B., 2011. Determinants of alliance portfolio complexity and its effect on innovative performance of companies. Journal of Product Innovation Management, 28(4): 570-85.
Du, J., Leten, B., Vanhaverbeke, W., 2014. Managing open innovation projects with science-based and market-based partners. Research Policy, 43: 828-840.
EPU 1986. Fifth Malaysia Plan. Economic Planning Unit, Putrajaya.
EPU 1990. Sixth Malaysia Plan. Economic Planning Unit, Putrajaya.
EPU 1996. Seventh Malaysia Plan. Economic Planning Unit, Putrajaya.
EPU 2001. Eighth Malaysia Plan. Economic Planning Unit, Putrajaya.
EPU 2006. Ninth Malaysia Plan. Economic Planning Unit, Putrajaya.
EPU 2010. Tenth Malaysia Plan, Economic Planning Unit, Putrajaya.
EPU 2013. Malaysian Economy in Figures 2013. Economic Planning Unit, Putrajaya.
EPU 2015. Eleventh Malaysia Plan. Economic Planning Unit, Putrajaya.
EPU 2015b. Services sector blueprint. Economic Planning Unit, Putrajaya.
283
Eisenhardt K. M., Tabrizi B. N., 1995. Accelerating adaptive processes: product innovation in the global computer industry. Administrative Science Quarterly, 40(1): 84–110.
Eriksson et al., 2016. Managing Interorganizational Innovation Projects: Mitigating the Negative Effects of Equivocality Through Knowledge Search Strategies. Long Range Planning, 49: 691-705. http://dx.doi.org/10.1016/j.lrp.2016.05.006
Eurostat 2014. Eurostat regional yearbook 2014, Luxembourg: Publications Office of the European Union.
Faems, D., Looy, B. Van & Debackere, K., 2005. Interorganizational collaboration and innovation: toward a portfolio approach. Journal of Product Innovation Management, 22, pp.238–250.
Felker, G., & K.S. Jomo., 2007. Technology policy in Malaysia, in: Jomo, K.S., Malaysian Industrial Policy, NUS Press, Singapore.
Ferreras-Méndez, J. L., Fernández-Mesa, A., Alegre, J., 2016. The relationship between knowledge search strategies and absorptive capacity: A deeper look. Technovation, 54: 48–61.
Figueiredo, P., 2002. Does technological learning pay off? Inter-firm differences in technological capability- accumulation paths and operational performance improvement, Research Policy, 31(1): 73-94.
Figueiredo, P., 2003. Learning, capability accumulation and firms differences: evidence from latecomer steel. Industrial and corporate change 12 (3), 607-643.
Figueiredo, P., 2008. Government Policies and Sources of Latecomer Firms' Capability Building: A Learning Story from Brazil. Oxford Development Studies, Taylor & Francis Journals, 36(1): 59-88.
Flick, U. (2007) Methodological Triangulation in Qualitative Research: Managing Quality in Qualitative Research . London SAGE Publication Ltd. Retrieved from https://methods.sagepub.com/book/managing-quality-in- qualitative-research
Freeman, C., 1987. Technology policy and economic performance: lessons from Japan. London, Pinter Publishers.
Freeman, C., Soete, L., 1997. The Economics of Industrial Innovation. Pinter: London.
Fritsch, M., Lukas, R., 2001. Who cooperates on R&D. Research Policy, 30: 297–312.
Gill, J., Johnson, P., 2010. Research Methods for Managers. London: SAGE Publications Ltd.
Gnyawali, D. R., Park, B., 2011. Co-opetition between giants: Collaboration with competitors for technological innovation. Research Policy, 40(5): 650–663.
Guimón, J., 2013. Promoting University-Industry Collaboration in Developing Countries. World Bank.
Guo, B., Wang, Y., Xie, X-Y., Shou, Y., 2015. Search more deeply or search more broadly? An empirical study of external knowledge search strategy in manufacturing SMEs, Asian Journal of Technology Innovation, 23(1): 87- 106. DOI: 10.1080/19761597.2015.1019177
Grant, R. M., Baden-Fuller, C., 2004. A knowledge accessing theory of strategic alliances. Journal of Management Studies, 41(1): 61-84.
Granovetter, M., 1973. The strength of weak ties. American Journal of Sociology, 78(6): 1360–1380.
Gulati, R., Lavie, D., Singh, H., 2009. The nature of partnering experience and the gains from alliances. Strategic Management Journal, 30: 1213–1233.
Gupta, A. K., Smith, K. G., Shalley, C. E., 2006. The interplay between exploration and exploitation. Academy of Management Journal, 49: 693–706.
284
Hamel, G., Doz, Y., Prahalad, C., 1989. Collaborate with your competitors and win. Harvard Business Review 67(1): 133–139.
Hair, J, F., Black, W, C., Babin, B, J., Anderson, R, E., 2010. Multivariate Data Analysis a Global Perspective, 7th edition. Upper Saddle River: Pearson.
Hamel, G., Prahalad, C., 1994. Competing For The Future. Harvard Business School Press Boston, Massachusetts.
Hansen. U., & David Ockwell. D., 2014. Learning and technological capability building in emerging economies: The case of the biomass power equipment industry in Malaysia. Technovation, 34(10), pp.617-630.
Heij, C, et al. 2004. Econometric methods with applications in business and economics. Oxford: Oxford University Press.
Hobday, M., 1994. Export-led technology development in the four dragons: the case of electronics, development and change, 25: 333–61.
Hobday, M., 1995. Innovation in East Asia: The Challenge to Japan.
Hsieh, K.N., Tidd, J., 2012. Open versus closed new service development: the influences of project novelty. Technovation, 32: 600–608.
Hsu, T., Tsai, K-H., Liao Y-C., 2013. How Knowledge Integration Mechanisms Affect Product Innovation in the NPD Process?. Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management, ToKnowPress. Iammarino, S. et al., 2012. Technological capabilities and patterns of innovative cooperation of firms in the UK regions. Regional Studies, 46(10), pp.1283–1301.
Iorio, R., Labory, S., Rentocchini, F., 2017. The importance of pro-social behaviour for the breadth and depth of knowledge transfer activities: An analysis of Italian academic scientists. Research Policy, 46: 497–509.
Jaramillo, H., Lugones, G., Salazar, M., 2001. Standardization of Indicators of Technological Innovation in Latin American and Carribean Countries. Bogotá Manual, RICYT/OAS/CYTED/COLCIENCIAS/OCYT.
Johnson, B., Turner, L., 2003. Data Collection Strategies in Mixed Methods Research’, in: A. Tashakkori and C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research, London: Sage Publications.
Jomo, K.S., 2014. Malaysia’s economic development and transformation, in: Basu Das, S. and P.O. Lee, Malaysia’s Socio-Economic Transformation: Ideas for the Next Decade, Institute of Southeast Asian Studies, Singapore.
Jomo, K.S., 2003. Southeast Asia’s Paper Tigers: From Miracle To Debacle And Beyond, Routledge Studies in the Growth Economies of Asia, Routledge, Abingdon-on-Thames.
Jorge Cruz-González, J; López-Sáez, P; Navas-López, J, E; Delgado-Verde, M (2015). Open search strategies and firm performance: The different moderating role of technological environmental dynamism. Technovation, 35: 32–45. http://dx.doi.org/10.1016/j.technovation.2014.09.001
Kaminski et al., 2008. Knowledge transfer in product development processes: A case study in small and medium enterprises (SMEs) of the metal – Mechanic sector from Sao Paulo, Technovation 28(1–2): 29–36.
Kanapathy, V., 2000. Industrial Restructuring in Malaysia: Policy Shifts and the Promotion of New Sources of Growth. Seiichi Masuyama, Donna Vandebrink and Chia Siow Yue (eds), Industrial Restructuring in East Asia, Singapore: Institute of Southeast Asian Studies, pp. 139–65.
Katila, R., 2002. New product search over time: past ideas in their prime? Academy of Management Journal 45, 995– 1010.
Katila, R. & Ahuja, G., 2002. Something old, something new: a longitudinal study of search behavior and new product introduction. Academy of Management Journal, 45(6), pp.1183–1194.
285
Katz. J. M., 1984. Domestic technological innovations and dynamic comparative advantage. Journal of Development Economics, 16, pp.13–37.
Katz, J., 2000. Structural Change and Labor Productivity Growth in Latin American Manufacturing Industries 1970– 1996. World Development, 28 (9): 1583–1596.
Kauffman, S., 1993. The Origins of Order. New York: Oxford University Press.
Kennedy, P., 2009. How to combine Multiple Research Methods: Practical Triangulation. Retrieved from http://johnnyholland.org/2009/08/practical-triangulation/
Khan, M., 2013. Technology Policies and Learning with Imperfect Governance. In: Stiglitz, Joseph and Lin, Justin Yifu, (eds.), The Industrial Policy Revolution I. The Role of Government Beyond Ideology. London: Palgrave, 79-115.
Kiamehr, M., Hobday, M., Kermanshahy, Ali., 2013. Latecomer systems integration capability in complex capital goods: the case of Iran’s electricity generation systems. Industrial and Corporate Change, 23(3): 689–716. doi:10.1093/icc/dtt036
Kim, L., 1980. Stages of development of industrial technology in a LDC: A model. Research Policy, 9: 254-277.
Kim, L., 1993. National System of Industrial Innovation: Dynamics of Capability Building in Korea, in Richard R. Nelson (Ed.), National Innovation Systems: A Comparative Analysis, pp. 357–383. New York: Oxford University Press. Kim, L., 1997. From imitation to innovation: The dynamics of Korea’s technological learning. Boston, MA: Harvard Business School Press.
Kim, L., 1998. Crisis Construction and Organizational Learning: Capability Building in Catching-up at Hyundai Motor. Organization Science, 9: 506–521.
Kim, L., 1999. The Impact of the Asian Crisis on National Innovation System: An Initial Assessment in Korea. Paper presented at the International Conference on Public Policy in the 21st Century on September 5–7, 1999 in Singapore.
Kim, L., & Nelson, R, R., 2000. Technology, learning, and innovation: experiences of Newly Industrializing Economies. Cambridge: Cambridge University Press.
Kim, L., & Nelson, R., 2000b. Technological Change and Industrialisation in the Asian Newly Industrialising Economies: Achievements and Challenges.
Kim. L., 2001. The Dynamics of Technological Learning in Industrialisation. Blackwell Publishers.
Krackhardt, D., 1992. The strength of strong ties: the importance of Philos in organizations. In: Nohria, N., Eccles, R.G. (Eds.), Networks and Organizations: Structure, Form, and Action. Harvard Business School Press, Boston, MA, 216–239.
Kumar, N., Siddharthan, N., 1997. Technology, Market Structure and Internationalization: Issues and Policies for Developing Countries (London/New York: Routledge and UNU Press).
Lall, S., 1992. Technological capabilities and industrialization. World Development, 20(2), pp.165–186.
Lall, S., 1993a. Understanding Technology Development. Development and Change, 24 (4), pp.719-53.
Lall, S., 1993b. Promoting Technology Development: The Role of Technology Transfer and Indigenous Effort. Third World Quarterly, 14(1), pp.95-108.
Lall, S., 1994. The East Asian Miracle. Study: Does the Bell Toll for Industrial Strategy? World Development. 22: 645–54.
286
Lall, S., Navaretti, S., Teitel., Wignaraja, G., 1994. Technology and Enterprise Development: Ghana Under Structural Adjustment. London: Macmillan.
Lall, S., 1995. Malaysia: Industrial success and the role of the government. Journal of International Development, 7(5), pp.759-773.
Lall. S., 1995b. Industrial Adaptation and Technological Capabilities in Developing Countries', in Tony Killick (ed.) The Flexible Economy: Causes and Consequences of the Adaptability of National Economies, London and New York: Routledge.
Lall, S., 2000a. In: Kim, L., Nelson, R. (Eds.), Technological Change and Industrialisation in the Asian Newly Industrialising Economies: Achievements and Challenges, pp. 13–68.
Lall, S., 2000b. The technological structure and performance of developing country manufactured exports, 1985-98. Oxford Development Studies, 28(3), pp.337–369.
Lamin, A. & Dunlap, D., 2011. Complex technological capabilities in emerging economy firms: The role of organizational relationships. Journal of International Management, 17, pp.211–228.
Laursen, K. & Salter, A., 2006. Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strategic Management Journal, 27, pp.131–150.
Laursen, K., 2012. Keep searching and you’ll find: what do we know about variety creation through firms’ search activities for innovation? Industrial and Corporate Change, 21: 1181–1220.
Laursen, K., Salter, A.J., 2014. The paradox of openness: Appropriability, external search and collaboration. Res. Policy 43, 867–878.
Lawrence Dooley, Breda Kenny & David O’Sullivan., 2017. Innovation capability development: case studies of small enterprises in the LMT manufacturing sector, Small Enterprise Research, 24:3: 233-256.
Lee, C., Lee, K., Pennings, M. J., 2001. Internal Capabilities, External Networks, And Performance: A Study On Technology- Based Ventures. Strategic Management, 22: 615–640. DOI: 10.1002/smj.181
Lee, N., Lings, I., 2008. Doing business research: a guide to theory and practice. London: SAGE.
Lee, C., 2008. The relationship between innovation, productivity and exports: some preliminary evidence from the Malaysian manufacturing sector. Economics Bulletin, 12 (31), pp.1-13.
Lee, C., 2012. Knowledge flows, organization and innovation: firm-level evidence from Malaysia. Working Papers DP-2012-11, Economic Research Institute for ASEAN and East Asia (ERIA).
Lee, H., Cho, E., Cheong, C., Kim, J., 2013. Do strategic alliances in a developing country create value? Evidence from Korean firms. Journal of Empirical Finance, 20: 30–41.
Leiponen, A., 2008. Control of intellectual assets in client relationships: implications for innovation. Strateg. Manage. J. 29, 1371–1394.
Leiponen, A., Helfat, C., 2010. Innovation Objectives, Knowledge Sources, And The Benefits Of Breadth. Strategic Management Journal, 31: 224–236.
Leiponen, A., 2012. The benefits of R&D and breadth in innovation strategies: a comparison of Finnish service and manufacturing firms, Industrial and Corporate Change, 21(5): 1255–1281. https://doi.org/10.1093/icc/dts022
Levinthal, D., March, J., 1981. A model of adaptive organizational search. Journal of Economic Behavior & Organization, 2: 307-33.
Levinthal, D., March, J., 1993. The myopia of learning (Special issue). Strategic Management Journal, 14: 95–112.
287
Lodh, S., Battaggion, M, R., (2014). Technological breadth and depth of knowledge in innovation: the role of mergers and acquisitions in biotech. Industrial and Corporate Change, 24(2): 383–415. doi:10.1093/icc/dtu013
Love, H. J., Roper, S., Vahter, P., 2014. Learning From Openness: The Dynamics Of Breadth In External Innovation Linkages. Strategic Management Journal, 35: 1703–1716.
Lööf, H., Broström, A., 2008. Does knowledge diffusion between university and industry increase innovativeness?. The Journal of Technology Transfer, 33(1), 73-90.
Lundvall, B-A., (ed.) (1992), National systems of innovation: towards a theory of innovation and interactive learning. London: Pinter Publishers.
Lundvall, B-A., Joseph, K., Chaminade, C., & Vang, J., editors (2009). Handbook of innovation systems and developing countries: building domestic capabilities in a global setting. Edward Elgar Publishing.
Malerba, F., 1992. Learning by firms and incremental technical change. The Economic Journal 102 (413), 845-859.
Malerba, F., Mani, S., (Eds.) 2009. Sectoral Systems of Innovation and Production in Developing Countries: Actors, Structure and Evolution. Edward Elgar Publishing, Cheltenham.
Malhotra, N., Birks, D., 2003. Marketing Research: An Applied Approach. London: Prentice Hall.
Mairesse, J., Pierre, M., 2010. Using innovation surveys for econometric analysis, in: Hall, B. H. and Rosenberg, N. (eds), Handbook of the Economics of Innovation, Elsevier, Amsterdam, 1130-1155.
Mairesse, J., Pierre M., 2009. Innovation surveys and innovation policy, in: D. Foray, ed, The New Economics of Technology Policy, Edward Elgar, Cheltenham, 215-230.
Malaysia SMEs (2015). SME development framework: The Malaysian case. http://www.smecorp.gov.my/
Marcelle, G., 2004. Technological learning: a strategic imperative for firms in the developing world. Edward Elgar Publishing, Cheltenham.
Marcelle, G., 2012. Editorial, International Journal of Technological Learning, Innovation and Development, Special Issue: Innovation for Development – Frontiers of Research, Policy and Practice, 5 (1-2): 1-11.
March, J., 1991. Exploration and exploitation in organizational learning. Organization Science, 2(1): 71–87.
MASTIC (1994, 1996, 1998, 2000, 2002, 2004, 2006, 2008, 2010, 2013). Malaysian Science & Technology Indicators Report, Ministry of Science, Technology and Innovation, Putrajaya.
MASTIC (2001, 2005, 2009, 2012, 2014, 2016), National Survey of Research and Development, Ministry of Science, Technology and Innovation, Putrajaya.
Mathews, J., 2002. Competitive Advantages of the Latecomer Firm: A Resource-Based Account of Industrial Catch- Up Strategies. Asia Pacific Journal of Management, 19: 467-488.
MATRADE 2015. Matrade introduces large corporations and SMEs Partnership Programme to stimulate services export”, News release, 14 December, Kuala Lumpur.
Maxwell, J. A., Loomis, D. M., 2003. Mixed-Methods Design: An Alternative Approach, in: A. Tashakkori and C. Teddlie (Eds.), Handbook of Mixed Methods in Social and Behavioral Research, London: Sage Publications.
Maxwell, J.A. (2011) Epistemological Heuristics for Qualitative Research. In H. Soini, E.L.-Kornqvist, & G.L. Huber (Eds), Epistemologies for Qualitative research Volume 3 (PP. 9-26) Germany Center for Qualitative Psychology. Retrieved from http://psydok.psycharchives.de/jspui/handle/20.500.11780/1398
Metcalfe, L., 1993. Public Management: From Imitation to Innovation. In Modern Governance (Jan Kooiman, ed.), pp. 173-189.
288
Metcalfe, J.S., 1994. Evolutionary economics and technology policy. Econ. J. 104 (425): 931–944.
Metcalfe, J.S., & R. Ramlogan., 2008. Innovation Systems and the Competitive Process in Developing Economies. The Quarterly Review of Economics and Finance, 48(2), pp. 433-446.
MIGHT 2009. Malaysia high technology 2009, MIGHT, Putrajaya.
MIGHT 2013. Science to Action (S2A): Building & sustaining a developed and prosperous Malaysia”, presentation at the S2A consultation 2013, 28 October, MIGHT, Putrajaya.
MIGHT 2013b. Science for industry, presentation at the S2A consultation 2013, 28 October, MIGHT, Putrajaya.
MITI 1986. Promotion of Investments Act 1986.
MITI 1996. Industrial Master Plan 1996-2005. Executive Summary,” Ministry of International Trade and Industry, Kuala Lumpur: Government Publication.
MITI 2016. Ministry of International Trade and Industry Malaysia Report 2016. http://www.miti.gov.my/miti/resources/MITI%20Report/MITI_REPORT_2016.pdf
MITI 2018. Ministry of International Trade and Industry Malaysia Report 2018.
Miles, M, B., Huberman, M, A., 1984. Qualitative Data Analysis: A Sourcebook of New Methods Sage Publications USA.
Miles, J., Shevlin, M., 2001. Applying regression & correlation: a guide for students and researchers. London; Thousand Oaks, Calif.: Sage Publications
Miotti. L., & Sachwald. F., 2003. Cooperative R&D: why and with whom? An integrated framework of analysis. Research Policy, 32(8), pp.1481-1499.
Miozzo, M. & Walsh, V., 2006. International competitiveness and technological change. Oxford: Oxford University Press.
Miozzo, M. & Grimshaw, D., 2011. Capabilities of large services outsourcing firms: the ‘outsourcing plus staff transfer model’ in EDS and IBM. Industrial and Corporate Change, 20(3), pp.909–940.
Miozzo, M., Desyllas, P., Lee, H., Miles, I., 2016. Innovation collaboration and appropriability by knowledge- intensive business services. Res. Policy 45, 1337–1351.
Miozzo, M., Divito, L., Desyllas, P., 2016 (b). When do acquirers invest in the R&D assets of acquired science-based firms in cross-border acquisitions? the role of technology and capabilities similarity and complementarity. Long Range Planning, 49, 221-240.
MNIS-1, 1994. National Survey Of Innovation 1994 Report. https://mastic.mestecc.gov.my/sti-survey-content- spds/national-survey-innovation-1994
MNIS-2, 1999. National Survey Of Innovation 1999 Report. https://mastic.mestecc.gov.my/sti-survey-content- spds/national-survey-innovation-1997-1999
MNIS-3, 2001. National Survey Of Innovation 2001 Report. https://mastic.mestecc.gov.my/sti-survey-content- spds/national-survey-innovation-2000-2001
MNIS-4, 2004. National Survey Of Innovation 2004 Report. https://mastic.mestecc.gov.my/sti-survey-content- spds/national-survey-innovation-2002-2004
MNIS-5, 2008. National Survey Of Innovation 2008 Report. https://mastic.mestecc.gov.my/sti-survey-content- spds/national-survey-innovation-2005-2008
289
MNIS-6, 2012. National Survey Of Innovation 2012 Report. https://mastic.mestecc.gov.my/sti-survey-content- spds/national-survey-innovation-2012
MOE 2015. Malaysia Education Blueprint 2015-2025 (Higher Education), Ministry of Higher Education, Putrajaya.
MOSTI 2003. Malaysia’s S&T policy for the 21st century. Ministry of Science, Technology and Innovation, Putrajaya.
MOSTI 2009. MOSTI in the Tenth Malaysia Plan. Ministry of Science, Technology and Innovation, Putrajaya.
MOSTI 2012. R&D funding mechanisms. Presentation at the Sixth ASIAHORCs General Meeting, 10 October, Beijing.
MOSTI 2013. National Policy on Science, Technology and Innovation 2013-2020. Ministry of Science, Technology and Innovation, Putrajaya.
MOSTI 2013b. Technofund: Grant opportunities for novel technology enterprise. Powerpoint presentation, Ministry of Science, Technology and Innovation, Putrajaya.
MOSTI 2015. Kemudahan Dana MOSTI (MOSTI Facilities Fund), Powerpoint presentation at the Technology Preview and Showcase Nuklear Malaysia, Agensi Nuklear Malaysia, Bangi, 27 October.
Molina-Domene. M., & Pietrobelli. C., 2012. Drivers of technological capabilities in developing countries: an econometric analysis of Argentina, Brazil and Chile. Structural Change and Economic Dynamics, 23, pp.504– 515.
M.Y. Hsu, L.K. Chen, H.C. Chuang, B.C. Shia, Y.T. Cheng, S.H. Wu., 2009. R&D cooperation linkages in Taiwan innovation system.
Mytelka, L., Goedhuys, M., Arundel, A., Gachino, G., 2004. Designing a Policy-Relevant Innovation Survey for NEPAD, A Study prepared for The Development Bank of Southern Africa Limited. United Nations University for New Technologies (UNU-INTECH).
Narula, R., 2004. R&D collaboration by SMEs: New opportunities and limitations in the face of globalization, Technovation 25, 153–161.
NEAC 2009. New Economic Model, www.epu.gov.my/en/new-economic-model.
NEAC 2013. New Economic Model for Malaysia, National Economic Advisory Council, Putrajaya.
Nelson, R. R., 1987. Innovation and Economic Development: Theoretical retrospect and prospect, in J. Katz (ed.), Technology Generation in Latin American Manufacturing Industries. London: Macmillan: 78–93.
Nelson, R. R & Winter, S. G., 1982. An evolutionary theory of economic change. Harvard University Press: Cambridge, MA.
Nelson, R R (ed.) (1993), National Innovation Systems. A Comparative Analysis. Oxford: Oxford University Press.
Nieto, M., & Santamaría, L., 2007. The importance of diverse collaborative networks for the novelty of product innovation. Technovation 27(6–7): 367–377.
Nieto, M. J., & Santamaria, L., 2010. Technological collaboration: Bridging the innovation gap between small and large firms. Journal of Small Business Management, 48(1), 44–69.
NSRC 2013. PRA Performance Evaluation: Unlocking Vast Potentials, Fast-Tracking the Future, National Science and Research Council, UiTM Press, Shah Alam.
Nylen, U., 2007. Interagency collaboration in human service: impact of formalization and intensity on effectiveness. Public Administration, 85: 143-166.
290
OECD 1992. Guidlines for Collecting and Interpreting Technological Innovation Data - The Oslo Manual, Paris, OECD.
OECD & EUROSTAT 1997. Guidlines for Collecting and Interpreting Technological Innovation Data - Oslo Manual, Second edition, Paris, OECD.
OECD & EUROSTAT 2005. Guidlines for Collecting and Interpreting Innovation Data; Oslo Manual, Third edition Paris, OECD.
OECD 2016. Innovation performance in Malaysia, in OECD Reviews of Innovation Policy: Malaysia 2016, OECD Publishing, Paris.
OECD 2013. Innovation in Southeast Asia, OECD Publishing, Paris.
OECD 2013b. OECD Investment Policy Reviews: Malaysia 2013. OECD Publishing, Paris.
Pei, D., Tseng, C., Liou, C., 2012. Collaborative innovation in emerging economies: The case of India and China. Innovation: Management, Policy & Practice, 14(3): 467–476. doi: 10.5172/impp.2012.14.3.467
Papke, L, E., Wooldridge, J, M., 1996. Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics 11, 619–632.
Patel, P., Van der Have, Robert, P., 2010. Enhancing Innovation Performance Through Exploiting Complementarity In Search Breadth And Depth. Frontiers of Entrepreneurship Research, 30 (9): Article 1. http://digitalknowledge.babson.edu/fer/vol30/iss9/1
Pavitt, K., 1984. Sectoral patterns of technical change: towards a taxonomy and a theory. Research Policy, 13, pp.343-373.
PEMANDU 2012. Government transformation programme: Annual report, 2012, Jabatan Perdana Menteri, Malaysia.
PEMANDU 2014. Economic Transformation Programme. Annual Report 2014, Performance Management & Delivery Unit.
Pietrobelli. C., 1998. The multiple linkages between technological change, growth and the economy. A Review of a Research Programme. LABOUR, CEIS, 12(2), pp.171-187.
Powell, W.W., Koput, K.W. & Smith-Doerr, L., 1996. Interorganizational collaboration and the locus of collaboration: networks of learning in biotechnology. Administrative Science Quarterly, 41, pp.116–145.
Quintana-García, C., Benavides-Velasco, C., 2004. Cooperation, competition, and innovative capability: a panel data of European dedicated biotechnology firms. Technovation, 24(12): 927–938.
Raghavendra, N, V., Subrahmanya, M, H, B., 2006. Development of a measure for technological capability in small firms Int. J. Innovation and Learning, 3(1), Inderscience Enterprises Ltd.
Rahman, H., 2013. Open Innovation in Entrepreneurships: Taxonomies of Innovation in Knowledge-Based Economy. International Journal of E-Entrepreneurship and Innovation, 4(3): 1-15, July-September 2013.
Rasiah, R. & Chandran, V.G.R., 2009. University-industry R&D collaboration in the automotive, biotechnology and electronics firms in Malaysia. Seoul journal of economics, 22(4), pp.529–550.
Rasiah, R., 1995. Foreign Capital and Industrialization in Malaysia. London: Macmillan.
Rasiah, R., 1996b. Innovations and Institutions: Moving Towards the Technological Frontier in the Electronics Industry in Malaysia”, Industry and Innovation, 3 (2): 79–102.
Rasiah, R., 1999. Malaysia’s national innovation system”, in: Felker, G. and K.S. Jomo, Technology, Competitiveness and the State: Malaysia’s Industrial Technology Policies, Routledge, Abingdon-on-Thames.
291
Rasiah, R., 2006. Electronics in Malaysia: Export expansion but slow technical change, in: World Bank, Technology, Adaptation, and Exports: How Some Developing Countries Got It, The World Bank, Washington, DC.
Rasiah, R., 2011. Is Malaysia facing negative deindustrialization?”, Pacific Affairs, Vol.84/4, December, University of British Columbia, Vancouver, pp.715-736.
Rasiah, R., & V.G.R. Chandran 2015. Malaysia, in: UNESCO, UNESCO Science Report: Towards 2030, UNESCO Publishing, Paris.
Reichert et al., (2011). Technological Capability’s Predictor Variables. Journal of Technology Management & Innovation, 6 (1): 14-25.
Reichert, F, M., & Zawislak, P, A., 2014. Technological Capability and Firm Performance. Journal of Technology Management & Innovation, 9(4), pp. 20-35.
Roth, S., 1999. The state of Design Research. Design Issues, Vol.15, No.2, Design research, pp 18-26.
Rosenberg, N., 1963. Technological change in the machine tool industry. Journal of Economic History, 23: 414–446.
Rothwell. R., Freeman, C., Jervis, P., Robertson, A., Townsend, J., 1974. SAPPHO updated: Project SAPPHO Phase 2. Research Policy 3(3): 258–291.
Salman, A., 2004. Elusive challenges of e-change management in developing countries, Business Process Management Journal 10(2), 140–157.
Sánchez-González, G., 2014. Cooperation with External Agents and Non-technological Innovations, in Hervás- Oliver, O; Peris-Ortiz, M (2014). Management Innovation: Antecedents, Complementarities and Performance Consequences. Springer Proceedings in Business and Economics, Springer International Publishing Switzerland 2014. http://www.springer.com/series/11960
Saunders, M., Lewis, P., Thornhill, A., 2003. Research Methods for Business Students. Harlow: Pearson Education Limited.
Saunders, M., et al. 2007. Research methods for business students. 4th ed. Harlow: FT Prentice Hall.
Saunders, M., Lewis, P., Thornhill, A., 2009. Research methods for business students, 5th ed., Harlow, Pearson Education.
Schmitz, H., 2006. The European Journal of Development Research, 18(4): 546–571.
Schmitz, H., Nadvi, K., 1999. Clustering and Industrialization: Introduction. World Development, 27(9): 1503-1514.
Schwandt, T. A., 2007. The SAGA Dictionary of Qualitative inquiry (3ed.) Thousand Oaks. California.
Scott‐Kemmis, D., Chitravas, C, 2007. Revisiting the learning and capability concepts – building learning systems in Thai auto component firms. Asian Journal of Technology Innovation 15 (2), 67–100.
Shan, J., Jolly, D., 2013. Technological innovation capabilities, product strategy, and firm performance: The electronics industry in China. Canadian Journal of Administrative Sciences Revue canadienne des sciences de l’administration 30: 159–172. DOI: 10.1002/CJAS.1256
Sheremata, W. A., 2000. Centrifugal and centripetal forces in radical new product development under time pressure. The Academy of Management Review, 25(2): 389-408.
SME Corporation 2006. SME Corporation Malaysia Innovation Development Programme, SME Corp, Kuala Lumpur.
SME Corporation 2014. SME Corp Annual Report 2014, SME Corporation, Kuala Lumpur.
292
SME Corporation 2018. SME Corp Annual Report 2018, SME Corporation, Kuala Lumpur.
Smith, K., 2005. Measuring Innovation, in Fagerberg J (ed) The Oxford Handbook of Innovation.
Smith, K., 2004. Measuring Innovation’, in Fagerberg, J., Mowery, D. and Nelson, R. (eds.), The Oxford Handbook of Innovation, Oxford University Press: Oxford.
Srinivasan, R., 2014. The management consulting industry: Growth of consulting services in India: Panel discussion. IIMB Management Review, 26(4): 257-270.
Sung, T-K., & Carlsson, B., 2007. Network effects, technological opportunity, and innovation: evidence from the Korean manufacturing firms. Asian Journal of Technology Innovation, 15, pp. 91-108.
Su, C, T., Parham, L, D., 2002. Case Report - Generating a valid questionnaire translation for cross-cultural use. American Journal of Occupational Therapy, 56: 581–585.
Suzuki, K., 1993. R&D Spillovers and Technology Transfer Among and Within Vertical Keiretsu Groups: Evidence from the Japanese Electrical Machinery Industry. Journal of Industrial Organisation, 11: 573–91.
Stanko, M. A., Henard, D, H., 2017. Toward a better understanding of crowdfunding, openness and the consequences for innovation. Research Policy, 46(4): 784-798. http://dx.doi.org/10.1016/j.respol.2017.02.003
Szogs, A., 2008. The role of mediator organizations in the making of innovation systems in least developed countries: evidence from Tanzania. Forthcoming In: Int. J. Technology and Globalization.
Tashakkori, A., Teddlie, C., 1998. Mixed Methodology: Combining Qualitative and Quantitative Approaches (Applied Social Research Methods). Thousand Oaks, California: Sage Publications Inc.
Teece. D. J., & Pisano, G., 1994. The dynamic capabilites of firms: an introduction. Industrial and Corporate Change, 3(3), pp.537-56.
Teece. D. J., Pisano, G. & Shuen, A., 1997. Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), pp.509–533.
Teddlie, C., Tashakkori, A., 2009. Foundations of mixed methods research. Thousand Oaks, CA: Sage.
Temel, S., Mention, A., Torkkeli, M., 2013. The Impact of Cooperation on Firms’ Innovation Propensity in Emerging Economies. Journal of Technology Management & Innovation, 8(1): 54-64.
Tether, B.S., 2002. Who co-operates for innovation, and why. An empirical analysis. Research Policy, 31, pp.947– 967.
Tether, B.S., Tajar, A., 2008. Beyond industry–university links: sourcing knowledge for innovation from consultants, private research organisations and the public science-base. Research Policy 37, 1079–1095.
Tidd, J. and J. Bessant, K. 2005. Managing Innovation: Integrating Technological, Market and Organizational Change. Chichester: John Wiley & Sons.
Tippmann, E., Sharkey Scott, P., Mangematin, V., 2014. Stimulating knowledge search routines and architecture competences: the role of organizational context and middle management. Long Range Planning, 47 (4): 206- 223.
Thiruchelvam, K., 2013. Malaysia’s quest for innovation”, presentation at 11th MP Kick Off conference, Pot Dickson, Universiti Teknologi Malaysia, Johor Bahru, 5 December.
Thiruchelvam, K., et al. 2011b. Innovation financing in Asia: The experience of Malaysia”, report prepared for The International Development Research Centre, Ottawa.
Tsai, K., 2004. The impact of technological capability on firm performance in Taiwan's electronics industry. Journal of High Technology Management Research, 15 (2), p.183-195.
293
Tumelero et al., 2018. The role of networks in technological capability: a technology-based companies perspective. Journal of Global Entrepreneurship Research (2018) 8:7 https://doi.org/10.1186/s40497-018-0095-5
UNCTAD 2003. Lall, S., Hamdi, M., 2003. United Nations Conference on Trade and Development: Division on Investment, Technology and Enterprise Development 2003.
UNIDO 1986. Industrial policy in the developing countries: An analysis of local content regulations.
UNIDO 2003. Malaysian Electronics: At the crossroads”, SME Technical Working Papers Series, No. 12, United Nations Industrial Development Organization, Vienna.
UNIDO & Harvard University 2007. Building Linkages for Competitive and Responsible Entrepreneurship: Innovative Partnerships to Foster Small Enterprise, Promote Economic Growth, and Reduce Poverty in Developing Countries, United Nations Industrial Development Organization, Geneva and the Fellows of Harvard College, Cambridge, Massachusetts, p.141.
United Nations Report (2017). The Sustainable Development Goals Report 2017. United Nations New York.
Uzzi, B., 1996. The sources and consequences of embeddedness for the economic performance of organizations: The network effect. American Sociological Review, 61(4): 674-698.
Uzzi, B., 1997. Social structure and competition in interfirm networks: The paradox of Embeddedness. Administrative Science Quarterly, 42(1): 35-67. van Wijk, R., Jansen, J. J., Van Den Bosch, F., Volberda, H. W., 2012. How firms shape knowledge to explore and exploit: a study of knowledge flows, knowledge stocks and innovative performance across units. Technology Analysis & Strategic Management, 24: 929–950.
Van Beers, C., Havas, A., Chiappero-Martinetti, E., 2015. Overview of Existing Innovation Indicators. CRESSI Working Paper no. 24/2015 – D3.3 (8 December 2015).
Veugelers. R., & Cassiman. B., 2005. R&D cooperation between firms and universities, some empirical evidence from Belgian manufacturing. International Journal of Industrial Organization, 23, pp.355–379. von Hippel, E., 1988. The sources of innovation. New York Oxford, Oxford University Press. von Hippel, E., 1976. The Dominant Role of Users in the Scientific Instrument Innovation Process. Research Policy, 5(3): 212–39. von Hippel, E., 1986. Lead users: a source of novel product concepts. Management Science, 32: 791–805.
Vrgovic, P., Vidicki, P., Glassman, B., Walton, A., 2012. Open innovation for SMEs in developing countries – An intermediated communication network model for collaboration beyond obstacles. Innovation: Management, Policy & Practice, 14(3): 290–302.
Wad, P., Chandran V,G,R., 2011. Automotive Industry in Malaysia: An Assessment of its Development, International Journal of Technology and Management, 11(2): 152-171.
Wang, G., Dou, W., Zhu, W., Zhou, N., 2015. The effects of firm capabilities on external collaboration and performance: The moderating role of market turbulence. Journal of Business Research, Elsevier, 68(9): 1928- 1936.
Wignaraja, G., 2001. Firm size, technological capabilities and market-oriented policies in Mauritius. UNU/INTECH Working Paper No. 1. Maastricht: UNU/INTECH.
Wignaraja. G., 1998. Competitiveness strategy in developing countries: A manual for policy analysis. Routledge Studies in Development Economics.
Wooldridge, J, M., 2002. Econometric analysis of cross section and panel data. Cambridge, MA: MIT Press.
294
Wong. K. P., 1999. Technological Capability Development by Firms from East Asian NIEs: Possible Lessons for Malaysia. Technology, Competitiveness and the State Malaysia's Industrial Technology Policies, pp.53-64. Eds Jomo K. S and Greg Felker. Routledge.
World Bank 2011. Malaysia economic monitor: Brain drain. The World Bank, Bangkok.
World Bank 2014. Facilitating global value chain integration for competitiveness in Costa Rica. Technical Assistance Summary Note TA P147207, The World Bank, Washington, DC.
World Bank 2017. World Bank Annual Report 2017 (English). Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/143021506909711004/World-Bank-Annual-Report-2017
World Economic Outlook, 2014. International Monetary Fund. 2014. World Economic Outlook: Legacies, Clouds, Uncertainties. Washington.
Wu, J., 2014. Cooperation with competitors and product innovation: Moderating effects of technological capability and alliances with universities. Industrial Marketing Management, 43: 199–209.
Yin, R. K., 2003. Case study research: Design and Methods, Sage Publications.
Young, A., 1996. Revising the Oslo Manual, International Conference on Innovation Measurement and Policies, 20– 21 May, Luxembourg, European Commission.
Yusoff, M., Abu H., Abdul, J, S., 2000. Globalisation, economic policy, and equity: the case of Malaysia. Documentation. OECD, Paris.
Yusoff, K.M., and V. Pillai 2014. Orchestrating a synchronised STI framework. MyForesight, January, MIGHT, Putrajaya.
Yusuf, S., & K., Nabeshima 2009. Tiger economies under threat: A comparative analysis of Malaysia’s industrial prospects and policy options”, The World Bank, Washington, DC.
Zang, J., Zhang, C., Yang, P., Li, Y., 2014. How open search strategies align with firms’ radical and incremental innovation: evidence from China. Technology Analysis & Strategic Management, 26: 781–795.
Zhang, J., 2016. Facilitating exploration alliances in multiple dimensions: the influences of firm technological knowledge breadth. R&D Management, 46(1): 159-173.
Zhang, J., Baden-Fuller, C., 2010. The influence of technological knowledge base and organizational structure on technology collaboration. Journal of Management Studies, 47: 679–704.
Zhou, K. Z., Li, C. B., 2010. How strategic orientations influence the building of dynamic capability in emerging economies. Journal Of Business Research, 63(3): 224–231.
295
APPENDICES
Appendix 1: Interview Protocol for Heads of R&D Departments and CEO / Company Directors. We are undertaking this study with a view to understanding how firms develop technological capability (TC) via inter-organizational collaboration (IOC) in Malaysia, in the context of emerging economies: how Malaysian manufacturing firms collaborate with external partners (customers, suppliers, consultants, private R&D, universities, government research institutes or competitor firms) in developing TC. The semi-formal interviews will take an hour to an hour and a half. All the information you provide will be remain confidential, along with your company name and your own name. You can provide your answers to the questions below based on your knowledge and expertise on the issues relating to your company. If you are unable to provide answers for any reason, including company confidentiality, this will be respected.
1. Does your company develop or build technological capability (TC)? In particular for the following activities: a. Does your company develop new or significantly improved products? b. Does your company develop new or significantly improved services? c. Does your company develop new or significantly improved methods of manufacturing or producing goods or services? d. Does your company develop new or significantly improved logistics, delivery or distribution methods for your inputs, goods or services? e. Does your company develop new or significantly improved supporting activities for your processes, such as maintenance systems or operations for purchasing, accounting, or computing? f. Does your company invest in R&D activities such as in-house R&D, acquisition of external R&D, acquisition of machinery, equipment and software, acquisition of external knowledge, market introduction of innovations, and other related activities.
2. Does your company collaborate or co-operate with any external partners to develop your own TC or the activities that you mention above? a. With whom do your collaborate mostly? b. What type of collaboration or relationship with those partners? (is it a formal type of relationship or common projects or sharing ideas)? Or anything else? c. Why does your company collaborate with these partners? d. Why is it important to collaborate with them? e. Among the partners that your company collaborates with, which type of partnership is most important for your company’s TC development? And why? f. Then the focus on seven partners: customers, suppliers, consultants, private R&D, universities, government research institutes, and competitors (to follow if they were not mentioned in previous conversation).
3. Does your company face any kind of barriers or difficulties in collaborating with external partners in efforts to develop TC? 4. How does your company manage the partnership relationship? a. How do you manage collaboration with many partners? b. What are the challenges your company faces to keep your collaboration networks active all the time? c. What is your longest and shortest collaboration for TC development?
5. Malaysian National Innovation System and Innovation policy in relation to TC development. a. What do you think about Malaysia’s innovation system and innovation policy as related to your company’s TC development or innovation activities? b. How do Malaysia’s innovation system and innovation policy help your company’s TC development? c. How might Malaysia’s innovation system and policy be improved in order to support your company’s TC development?
296
Appendix 2: Interview Protocol for Policy Makers and Government Authority
From policy perspectives, how do firms in Malaysia develop technological capability (TC)? Is inter- organizational collaboration (IOC) important for building TC?
What current policies are in place to support TC developments among manufacturing firms in Malaysia?
To what extent do the policies and strategies support TC development, in particular from IOC?
How do innovation policies promote TC development and IOC among manufacturing firms in Malaysia?
How does government manage the challenges in order to provide a platform and support for manufacturing firms to develop TC through IOC?
How often does government make changes to policies in order to supports manufacturing TC development?
In relation to firms’ TC successes or failures, from a policy perspective what are the main barriers to manufacturing firms in developing TC in Malaysia?
Why are manufacturing firms left behind in terms of TC development compared to developed countries (for example Singapore)?
297
Appendix 3: The Coding for R (Logistic Regression and Robustness Test).
***** Thesis: Technological Capability and Inter-Organizational Collaboration***** ********************in an Emerging Economy ****************************
***********************Rajenthyran Ayavoo*****************************
R coding for Logistic Regression ##############DATA ANALYSIS################################## ###### Load Packages ###### #install.packages("reshape2") library ("plyr") library ("dplyr") library ("ggplot2") library ("glmnet") library ("lattice") library ("party") library ("Boruta") library ("DMwR") library ("mice") library ("hdlm") library ("psych") library ("lmtest") library ("sandwich") library ("dummies") library ("DataCombine") library ("covTest") library ("plm") library ("texreg") library ("xtable") library ("Hmisc") library ("REEMtree") library ("glmnet") library ("plspm") library ("tydir") library ("pastecs") library ("ggm") library ("psych") library ("aod") library ("ggplot2")
########################################### ####### 1 User defined functions ######### ###########################################
## Remove the NAs from the new variables completeFun <- function(data, desiredCols) { completeVec <- complete.cases(data[, desiredCols]) return(data[completeVec, ]) } remove_outliers <- function(x, na.rm = TRUE, ...) { qnt <- quantile(x, probs=c(.25, .75), na.rm = na.rm, ...)
298
H <- 1.5 * IQR(x, na.rm = na.rm) y <- x y[x < (qnt[1] - H)] <- NA y[x > (qnt[2] + H)] <- NA y }
#write.csv(df ,file="P:ceoPay/ceoData.csv") ################################################ ############## Import Data ######################## ################################################ # raj_data <- read.csv("P:0 rfiles//rajan/rData911.csv", na.strings=c(".", "NA")) # # raj_data <- read.csv("E:rajan/rData911.csv")
#raj_data <- read.csv("P:0 rfiles//rajan/newData.csv", na.strings=c(".", "NA")) raj_data <- read.csv("/Users/arajen/Desktop/Ronnie/Dr.Reza/Rajen/NewR1.csv", na.strings=c(".", "NA")) # raj_data <- read.csv("E:rajan/rData911.csv") names(raj_data) sapply(raj_data, class) fix(raj_data)
#########Renaming variables################################### ############################################################## raj_data1 <-dplyr::rename(raj_data, SuppliersBre = Suppliers..Bre., SuppliersDep = Suppliers..Dep., CustomersBre = Customers..Bre., CustomersDep = Customers..Dep., CompetitorsBre = Competitors..Bre., CompetitorsDep = Competitors..Dep., ConsultantsBre = Consultants..Bre., ConsultantsDep = Consultants..Dep., PrivateRDBre = Private.R.D..Bre., PrivateRDDep = Private.R.D..Dep., UniversitiesBre = Universities..Bre., UniversitiesDep =Universities..Dep., GovernmentBre = Government..Bre., GovernmentDep = Government..Dep.)
### create firm id ...... raj_data1<-separate(raj_data1, Firms, into = c("Name", "id")) names(raj_data1) sapply(raj_data1, class)
########################## #### Recode variables #### ########################## raj_data1$Sectors <- as.factor(raj_data1$Sectors) raj_data1$Year <- as.factor(raj_data1$Year) raj_data1$TC1 <- as.factor(raj_data1$TC1) raj_data1$TC2 <- as.factor(raj_data1$TC2) raj_data1$TC3 <- as.factor(raj_data1$TC3) raj_data1$TC4 <- as.factor(raj_data1$TC4) raj_data1$TC5 <- as.factor(raj_data1$TC5)
299
raj_data1$TCIN1 <- as.factor(raj_data1$TCIN1) raj_data1$TCIN2 <- as.factor(raj_data1$TCIN2) raj_data1$TCIN3 <- as.factor(raj_data1$TCIN3) raj_data1$TCIN4 <- as.factor(raj_data1$TCIN4) raj_data1$TCIN5 <- as.factor(raj_data1$TCIN5) raj_data1$TCIN6 <- as.factor(raj_data1$TCIN6) raj_data1$TCIN7 <- as.factor(raj_data1$TCIN7) raj_data1$TCIN8 <- as.factor(raj_data1$TCIN8) raj_data1$TCIN9 <- as.factor(raj_data1$TCIN9) raj_data1$SuppliersBre <- as.factor(raj_data1$SuppliersBre) raj_data1$SuppliersDep <- as.factor(raj_data1$SuppliersDep) raj_data1$CustomersBre <- as.factor(raj_data1$CustomersBre) raj_data1$CustomersDep <- as.factor(raj_data1$CustomersDep) raj_data1$CompetitorsBre <- as.factor(raj_data1$CompetitorsBre) raj_data1$CompetitorsDep <- as.factor(raj_data1$CompetitorsDep) raj_data1$ConsultantsBre <- as.factor(raj_data1$ConsultantsBre) raj_data1$PrivateRDBre <- as.factor(raj_data1$PrivateRDBre) raj_data1$PrivateRDDep <- as.factor(raj_data1$PrivateRDDep) raj_data1$UniversitiesBre <- as.factor(raj_data1$UniversitiesBre) raj_data1$UniversitiesDep <- as.factor(raj_data1$UniversitiesDep) raj_data1$GovernmentBre <- as.factor(raj_data1$GovernmentBre) raj_data1$GovernmentDep <- as.factor(raj_data1$GovernmentDep)
fix(raj_data1)
############################################################## #############################Breadth Partner##################### ############################################################## raj_data <- raj_data%>% mutate(sup1 = (ifelse(Suppliers > 0, 1, ifelse(Suppliers == "", NA, 0))))%>% mutate(cust1 = (ifelse(Customers > 0, 1, ifelse(Customers == "", NA, 0))))%>% mutate(comp1 = (ifelse(Competitors > 0, 1, ifelse(Competitors == "", NA, 0))))%>% mutate(con1 = (ifelse(Consultants > 0, 1, ifelse(Consultants == "", NA, 0))))%>% mutate(rd1 = (ifelse(Private.R.D > 0, 1, ifelse(Private.R.D == "", NA, 0))))%>% mutate(uni1 = (ifelse(Universities > 0, 1, ifelse(Universities == "", NA, 0))))%>% mutate(gov1 = (ifelse(Government > 0, 1, ifelse(Government == "", NA, 0)))) raj_data <- raj_data%>% mutate(breadthPart = ((sup1 + comp1 + con1 + cust1 + rd1 + uni1 + gov1))/7) raj_data <- raj_data%>% mutate(breadthPartValue = ((sup1 + comp1 + con1 + cust1 + rd1 + uni1 + gov1))/1) raj_data <- raj_data%>% mutate(breadthPartValue2 = (((sup1 + comp1 + con1 + cust1 + rd1 + uni1 + gov1))/1)^2)
300
##################### ### Depth Partner###### ##################### raj_data <- raj_data%>% mutate(sup11 = (ifelse(Suppliers > 2, 1, ifelse(Suppliers == "", NA, 0))))%>% mutate(cust11 = (ifelse(Customers > 2, 1, ifelse(Customers == "", NA, 0))))%>% mutate(comp11 = (ifelse(Competitors > 2, 1, ifelse(Competitors == "", NA, 0))))%>% mutate(con11 = (ifelse(Consultants > 2, 1, ifelse(Consultants == "", NA, 0))))%>% mutate(rd11 = (ifelse(Private.R.D > 2, 1, ifelse(Private.R.D == "", NA, 0))))%>% mutate(uni11 = (ifelse(Universities > 2, 1, ifelse(Universities == "", NA, 0))))%>% mutate(gov11 = (ifelse(Government > 2, 1, ifelse(Government == "", NA, 0)))) raj_data <- raj_data%>% mutate(depthPart = ((sup11 + comp11 + con11 + cust11 + rd11 + uni11 + gov11))/7) raj_data <- raj_data%>% mutate(depthPartValue = ((sup11 + comp11 + con11 + cust11 + rd11 + uni11 + gov11))/1) raj_data <- raj_data%>% mutate(depthPartVertical = ((sup11 + cust11))/2) raj_data <- raj_data%>% mutate(depthPartKIB = ((con11 + rd11 + uni11 + gov11))/4)
raj_data <- raj_data%>% mutate(depthPartValue2 = (((sup11 + comp11 + con11 + cust11 + rd11 + uni11 + gov11))/1)^2) fix(raj_data)
######################################################################### ### Breadth Source (alternative to the independent Variable or Source of information for innovation activities) ######################################################## ########################################################################## raj_data <- raj_data%>% mutate(sups1 = (ifelse(Source.Suppliers > 0, 1, ifelse(Source.Suppliers == "", NA, 0))))%>% mutate(custs1 = (ifelse(Source.Customers > 0, 1, ifelse(Source.Customers == "", NA, 0))))%>% mutate(comps1 = (ifelse(Source.Competitors > 0, 1, ifelse(Source.Competitors == "", NA, 0))))%>% mutate(cons1 = (ifelse(Source.Consultants > 0, 1, ifelse(Source.Consultants == "", NA, 0))))%>% mutate(rds1 = (ifelse(Source.Private.R.D > 0, 1, ifelse(Source.Private.R.D == "", NA, 0))))%>% mutate(unis1 = (ifelse(Source.Universities > 0, 1,
301
ifelse(Source.Universities == "", NA, 0))))%>% mutate(govs1 = (ifelse(Source.Government > 0, 1, ifelse(Source.Government == "", NA, 0)))) raj_data <- raj_data%>% mutate(breadthSource = ((sups1 + comps1 + cons1 + custs1 + rds1 + unis1 + govs1))/7)
####################################################################### ### Depth Source (alternative to the independent Variable or Source of information for innovation activities)###################################################### ####################################################################### raj_data <- raj_data%>% mutate(sups11 = (ifelse(Source.Suppliers > 2, 1, ifelse(Source.Suppliers == "", NA, 0))))%>% mutate(custs11 = (ifelse(Source.Customers > 2, 1, ifelse(Source.Customers == "", NA, 0))))%>% mutate(comps11 = (ifelse(Source.Competitors > 2, 1, ifelse(Source.Competitors == "", NA, 0))))%>% mutate(cons11 = (ifelse(Source.Consultants > 2, 1, ifelse(Source.Consultants == "", NA, 0))))%>% mutate(rds11 = (ifelse(Source.Private.R.D > 2, 1, ifelse(Source.Private.R.D == "", NA, 0))))%>% mutate(unis11 = (ifelse(Source.Universities > 2, 1, ifelse(Source.Universities == "", NA, 0))))%>% mutate(govs11 = (ifelse(Source.Government > 2, 1, ifelse(Source.Government == "", NA, 0))))%>% raj_data <- raj_data%>% mutate(depthSource = ((sups11 + comps11 + cons11 + custs11 + rds11 + unis11 + govs11))/7)
##################################################### ###################Depth Sources Partners ############### ########### ########################################## raj_data <- raj_data%>% mutate(deptsourcesupcus = ((sups11 + comps11 + cons11 + custs11 + rds11 + unis11 + govs11))/7) mutate(smuSupCus = ((sups11 + comps11))%>% mutate(depthSupCus = ifelse(smuSupCus > 2, ifelse(smuSupCus == "", NA, 0, 1))) raj_data <- raj_data%>% mutate(depthSourceCal = ((sups11 + comps11 + cons11 + custs11 + rds11 + unis11 + govs11)))%>% mutate(depthSourceLow = (ifelse(depthSourceCal <3, 1, ifelse(depthSourceCal == "", NA, 0))))%>% mutate(depthSourceMed = (ifelse(depthSourceCal >2 & depthSourceCal <6, 1, ifelse(depthSourceCal == "", NA, 0))))%>% mutate(depthSourceHigh = (ifelse(breadthSourceCal >5, 1, ifelse(depthSourceCal == "", NA, 0))))
########################## #### Dependent variable ###### ########################## raj_data <- raj_data%>%
302
mutate(smuTC = (TC1 + TC2 + TC3 + TC4 +TC5))%>% mutate(newDep = ifelse(smuTC > 0, 1, ifelse(smuTC == "", NA, 0))) raj_data <- raj_data%>% mutate(smuTC = (TC1 + TC2 + TC3 + TC4 +TC5))%>% mutate(newDep00 = ifelse(smuTC > 1, 1, ifelse(smuTC == "", NA, 0))) raj_data <- raj_data%>% mutate(smuTC = (TC1 + TC2 + TC3 + TC4 +TC5))%>% mutate(newDep3 = ifelse(smuTC > 2, 1, ifelse(smuTC == "", NA, 0))) raj_data <- raj_data%>% mutate(Dep = ((TC1 + TC2 + TC3 + TC4 +TC5))/5) raj_data <- raj_data%>% mutate(DepIn = ((TCIN1 + TCIN2 + TCIN3 + TCIN4 + TCIN5 + TCIN6 +TCIN7 + TCIN8 + TCIN9))/9) raj_data <- raj_data%>% mutate(smuTCIN = (TCIN1 + TCIN2 + TCIN3 + TCIN4 + TCIN5 + TCIN6 +TCIN7 + TCIN8 + TCIN9))%>% mutate(newDep1 = ifelse(smuTCIN > 0, 1, ifelse(smuTCIN == "", NA, 0))) raj_data <- raj_data%>% mutate(smuTCIN1 = (TCIN1 + TCIN2 + TCIN3 + TCIN4 + TCIN5 + TCIN6 +TCIN7 + TCIN8 + TCIN9))%>% mutate(newDep5 = ifelse(smuTCIN1 > 1, 1, ifelse(smuTCIN == "", NA, 0))) raj_data <- raj_data%>% mutate(smuTCIN1 = (TCIN1 + TCIN2 + TCIN3 + TCIN4 + TCIN5 + TCIN6 +TCIN7))%>% mutate(newDep50 = ifelse(smuTCIN1 > 0, 1, ifelse(smuTCIN == "", NA, 0))) raj_data <- raj_data%>% mutate(smuTCIN1 = (TCIN1 + TCIN2 + TCIN3 + TCIN4 + TCIN5 + TCIN6 +TCIN7))%>% mutate(newDep500 = ifelse(smuTCIN1 > 1, 1, ifelse(smuTCIN == "", NA, 0))) raj_data <- raj_data%>% mutate(smuTCIN1 = (TCIN1 + TCIN2 + TCIN3 + TCIN4 + TCIN5 + TCIN6 +TCIN7))%>% mutate(newDepTC2 = ifelse(smuTCIN1 > 2, 1, ifelse(smuTCIN == "", NA, 0)))
raj_data <- raj_data%>% mutate(smuTCIN = (TCIN1 + TCIN2 + TCIN3 + TCIN4 + TCIN5 + TCIN6 +TCIN7 + TCIN8 + TCIN9))%>% mutate(newDep55 = ifelse(smuTCIN > 2, 1, ifelse(smuTCIN == "", NA, 0)))
303
raj_data <- raj_data%>% mutate(TCV = ((TC1 + TC2 + TC3 + TC4 +TC5))/1) raj_data <- raj_data%>% mutate(TCINV = ((TCIN1 + TCIN2 + TCIN3 + TCIN4 + TCIN5 + TCIN6 +TCIN7 + TCIN8 + TCIN9))/1)
raj_data <- raj_data%>% mutate(smuTCIN = (TCIN1 + TCIN2))%>% mutate(TCINRD = ifelse(smuTCIN > 0, 1, ifelse(smuTCIN == "", NA, 0))) raj_data <- raj_data%>% mutate(smuTCIN = (TCIN3 + TCIN4 + TCIN5 + TCIN6 +TCIN7 + TCIN8 + TCIN9))%>% mutate(TCINRD1 = ifelse(smuTCIN > 0, 1, ifelse(smuTCIN == "", NA, 0)))
summary (raj_data1$breadthPart) summary (raj_data1$depthPart) summary (raj_data1$newDep) summary (raj_data1$newDep1) summary (raj_data1$Sub.Sectors1) summary (raj_data1$Size1) summary (raj_data1$Year)
####################### ### Full data############ ###################### raj_data1 <- raj_data
prefix1 <- "newDep~" prefix1 <- "newDep00~" prefix1 <- "newDep5~" prefix1 <- "newDep55~"
prefix1 <- "TCINRD~" prefix1 <- "TCINRD1~" prefix1 <- "newDep50~" prefix1 <- "newDep500~" prefix1 <- "newDepTC2~" set1 <- dplyr::select(raj_data1, Sub.Sectors1, Size1, Year, Ownership1) set2 <- dplyr::select(raj_data1, breadthPart) set3 <- dplyr::select(raj_data1, depthPart)
formula1 <- as.formula(paste(prefix1, paste(c(names(set1)), collapse = "+"))) formula2 <- as.formula(paste(prefix1, paste(c(names(set1), names(set2)), collapse = "+"))) formula3 <- as.formula(paste(prefix1, paste(c(names(set1), names(set3)), collapse = "+")))
304
formula4 <- as.formula(paste(prefix1, paste(c(names(set1), names(set2), names(set3)), collapse = "+"))) fit1 <- glm(formula1, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1)
fit2 <- glm(formula2, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) fit3 <- glm(formula3, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) fit4 <- glm(formula4, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) texreg(list(fit1, fit2, fit3, fit4), digits = 3, caption.above = TRUE, booktabs = TRUE, dcolumn = TRUE, scalebox = 0.73, custom.model.names = c("(1)", "(2)", "(3)", "(4)"), single.row = TRUE, stars = c(0.01, 0.05, 0.1))
################################################# ###########Individual Sources Collaboration######## ############################################### ### Full data raj_data1 <- raj_data prefix1 <- "newDep~" prefix1 <- "newDep00~" prefix1 <- "newDep5~" prefix1 <- "newDep55~" prefix1 <- "TCINRD~" prefix1 <- "TCINRD1~" set1 <- dplyr::select(raj_data1, Sub.Sectors1, Size1, Year, Ownership1) set51 <- dplyr::select(raj_data1, sup11) set52 <- dplyr::select(raj_data1, cust11) set53 <- dplyr::select(raj_data1, comp11) set54 <- dplyr::select(raj_data1, con11) set55 <- dplyr::select(raj_data1, rd11) set56 <- dplyr::select(raj_data1, uni11) set57 <- dplyr::select(raj_data1, gov11)
formula50 <- as.formula(paste(prefix1, paste(c(names(set1)), collapse = "+"))) formula51 <- as.formula(paste(prefix1, paste(c(names(set1), names(set51)), collapse = "+")))
305
formula52 <- as.formula(paste(prefix1, paste(c(names(set1), names(set52)), collapse = "+"))) formula53 <- as.formula(paste(prefix1, paste(c(names(set1), names(set53)), collapse = "+"))) formula54 <- as.formula(paste(prefix1, paste(c(names(set1), names(set54)), collapse = "+"))) formula55 <- as.formula(paste(prefix1, paste(c(names(set1), names(set55)), collapse = "+"))) formula56 <- as.formula(paste(prefix1, paste(c(names(set1), names(set56)), collapse = "+"))) formula57 <- as.formula(paste(prefix1, paste(c(names(set1), names(set57)), collapse = "+")))
fit50 <- glm(formula50, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit50)
fit51 <- glm(formula51, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit51)
fit52 <- glm(formula52, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit52)
fit53 <- glm(formula53, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit53)
fit54 <- glm(formula54, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit54) fit55 <- glm(formula55, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit55) fit56 <- glm(formula56, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit56)
306
fit57 <- glm(formula57, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit57)
texreg(list(fit50, fit51, fit52, fit53, fit54, fit55, fit56, fit57), digits = 3, caption.above = TRUE, booktabs = TRUE, dcolumn = TRUE, scalebox = 0.73, custom.model.names = c("(1)", "(2)", "(3)", "(4)", "(5)", "(6)", "(7)", "(8)"), single.row = TRUE, stars = c(0.01, 0.05, 0.1))
########################################### ###########Vertical, Horizintal, KIB ########### ###########################################
### Full data######## raj_data1 <- raj_data prefix1 <- "newDep~" prefix1 <- "newDep00~" prefix1 <- "newDep5~" prefix1 <- "newDep55~" prefix1 <- "TCINRD~" prefix1 <- "TCINRD1~" set1 <- dplyr::select(raj_data, Sub.Sectors1, Size1, Year, Ownership1) set51 <- dplyr::select(raj_data, depthPartVertical) set52 <- dplyr::select(raj_data, comp11) set53 <- dplyr::select(raj_data, depthPartKIB) formula50 <- as.formula(paste(prefix1, paste(c(names(set1)), collapse = "+"))) formula51 <- as.formula(paste(prefix1, paste(c(names(set1), names(set51)), collapse = "+"))) formula52 <- as.formula(paste(prefix1, paste(c(names(set1), names(set52)), collapse = "+"))) formula53 <- as.formula(paste(prefix1, paste(c(names(set1), names(set53)), collapse = "+"))) fit50 <- glm(formula50, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit50)
fit51 <- glm(formula51, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit51)
fit52 <- glm(formula52, family=binomial(link='logit'), control = list(maxit = 500),
307
data=raj_data1) summary(fit52)
fit53 <- glm(formula53, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data1) summary(fit53)
texreg(list(fit50, fit51, fit52, fit53), digits = 3, caption.above = TRUE, booktabs = TRUE, dcolumn = TRUE, scalebox = 0.73, custom.model.names = c("(1)", "(2)", "(3)", "(4)"), single.row = TRUE, stars = c(0.01, 0.05, 0.1))
############################################################## #############Vertical, Competitors, KIB ########################### ############################################################## set1 <- dplyr::select(raj_data3, Sub.Sectors, Year) set51 <- dplyr::select(raj_data3, depthPartVertical) set52 <- dplyr::select(raj_data3, comp11) set53 <- dplyr::select(raj_data3, depthPartKIB) formula50 <- as.formula(paste(prefix1, paste(c(names(set1)), collapse = "+"))) formula51 <- as.formula(paste(prefix1, paste(c(names(set1), names(set51)), collapse = "+"))) formula52 <- as.formula(paste(prefix1, paste(c(names(set1), names(set52)), collapse = "+"))) formula53 <- as.formula(paste(prefix1, paste(c(names(set1), names(set53)), collapse = "+"))) fit50 <- glm(formula50, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data3) summary(fit50)
fit51 <- glm(formula51, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data3) summary(fit51)
fit52 <- glm(formula52, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data3) summary(fit52)
fit53 <- glm(formula53, family=binomial(link='logit'), control = list(maxit = 500), data=raj_data3)
308
summary(fit53)
texreg(list(fit50, fit51, fit52, fit53), digits = 3, caption.above = TRUE, booktabs = TRUE, dcolumn = TRUE, scalebox = 0.73, custom.model.names = c("(1)", "(2)", "(3)", "(4)"), single.row = TRUE, stars = c(0.01, 0.05, 0.1))
pcor(c("newDep", "newDep5", "Sub.Sectors1", "Size1", "Year", "Ownership1"), var(raj_data1))
set1 <- dplyr::select(raj_data1, Sub.Sectors1, Size1, Year, Ownership1) set2 <- dplyr::select(raj_data1, breadthPartValue) set3 <- dplyr::select(raj_data1, depthPartValue)
########################################################### ############### Correlation between variables################### ########################################################### myVectors = c("newDep", "newDep5", "breadthPart", "depthPart","sup11", "cust11", "comp11", "con11", "rd11", "uni11", "gov11", "Sub.Sectors1", "Size1", "Year", "Ownership1") cor(raj_data1[myVectors], use = "complete.obs") psych::alpha(raj_data1[myVectors], check.keys = TRUE) alpha(raj_data1[myVectors]) your.data = read.csv(file.choose()) = cor(var1, var2, method = "pearson") cor.test(newDep, newDep5)
myVectors = c("newDep", "newDep5", "breadthPart", "depthPart","sup11", "cust11", "comp11", "con11", "rd11", "uni11", "gov11", "Sub.Sectors1", "Size1", "Year", "Ownership1") cor(raj_data1[myVectors], use = "complete.obs") psych::alpha(raj_data1[myVectors], method = "spearman") alpha(raj_data1[myVectors]) myVectors = c("newDep", "newDep5", "breadthPart", "depthPart","sup11", "cust11", "comp11", "con11", "rd11", "uni11", "gov11", "Sub.Sectors1", "Size1", "Year", "Ownership1") cor(raj_data1[myVectors], method="pearson") psych::alpha(raj_data1[myVectors], check.keys = TRUE) alpha(raj_data1[myVectors])
###################################### #######Probit Model################## ######################################
### Full data raj_data1 <- raj_data
prefix1 <- "newDep~" prefix1 <- "newDep00~" prefix1 <- "newDep5~" prefix1 <- "newDep55~"
309
prefix1 <- "TCINRD~" prefix1 <- "TCINRD1~" prefix1 <- "newDep50~" prefix1 <- "newDep500~" prefix1 <- "newDepTC2~" set1 <- dplyr::select(raj_data1, Sub.Sectors1, Size1, Year, Ownership1) set2 <- dplyr::select(raj_data1, breadthPart) set3 <- dplyr::select(raj_data1, depthPart)
formula1 <- as.formula(paste(prefix1, paste(c(names(set1)), collapse = "+"))) formula2 <- as.formula(paste(prefix1, paste(c(names(set1), names(set2)), collapse = "+"))) formula3 <- as.formula(paste(prefix1, paste(c(names(set1), names(set3)), collapse = "+"))) formula4 <- as.formula(paste(prefix1, paste(c(names(set1), names(set2), names(set3)), collapse = "+"))) fit1 <- glm(formula1, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) fit2 <- glm(formula2, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) fit3 <- glm(formula3, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) fit4 <- glm(formula4, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) texreg(list(fit1, fit2, fit3, fit4), digits = 3, caption.above = TRUE, booktabs = TRUE, dcolumn = TRUE, scalebox = 0.73, custom.model.names = c("(1)", "(2)", "(3)", "(4)"), single.row = TRUE, stars = c(0.01, 0.05, 0.1))
############################################ ######Probit Individual Partners################# ############################################ raj_data1 <- raj_data prefix1 <- "newDep~" prefix1 <- "newDep00~" prefix1 <- "newDep5~" prefix1 <- "newDep55~"
set1 <- dplyr::select(raj_data1, Sub.Sectors1, Size1, Year, Ownership1) set51 <- dplyr::select(raj_data1, sup11)
310
set52 <- dplyr::select(raj_data1, cust11) set53 <- dplyr::select(raj_data1, comp11) set54 <- dplyr::select(raj_data1, con11) set55 <- dplyr::select(raj_data1, rd11) set56 <- dplyr::select(raj_data1, uni11) set57 <- dplyr::select(raj_data1, gov11)
formula50 <- as.formula(paste(prefix1, paste(c(names(set1)), collapse = "+"))) formula51 <- as.formula(paste(prefix1, paste(c(names(set1), names(set51)), collapse = "+"))) formula52 <- as.formula(paste(prefix1, paste(c(names(set1), names(set52)), collapse = "+"))) formula53 <- as.formula(paste(prefix1, paste(c(names(set1), names(set53)), collapse = "+"))) formula54 <- as.formula(paste(prefix1, paste(c(names(set1), names(set54)), collapse = "+"))) formula55 <- as.formula(paste(prefix1, paste(c(names(set1), names(set55)), collapse = "+"))) formula56 <- as.formula(paste(prefix1, paste(c(names(set1), names(set56)), collapse = "+"))) formula57 <- as.formula(paste(prefix1, paste(c(names(set1), names(set57)), collapse = "+")))
fit50 <- glm(formula50, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit50)
fit51 <- glm(formula51, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit51)
fit52 <- glm(formula52, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit52)
fit53 <- glm(formula53, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit53)
fit54 <- glm(formula54, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit54) fit55 <- glm(formula55, family=binomial(link='probit'),
311
control = list(maxit = 500), data=raj_data1) summary(fit55) fit56 <- glm(formula56, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit56) fit57 <- glm(formula57, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit57)
texreg(list(fit50, fit51, fit52, fit53, fit54, fit55, fit56, fit57), digits = 3, caption.above = TRUE, booktabs = TRUE, dcolumn = TRUE, scalebox = 0.73, custom.model.names = c("(1)", "(2)", "(3)", "(4)", "(5)", "(6)", "(7)", "(8)"), single.row = TRUE, stars = c(0.01, 0.05, 0.1))
################################################# ###########Vertical, Horizintal, KIB ################## ################################################# ### Full data raj_data1 <- raj_data prefix1 <- "newDep~" prefix1 <- "newDep00~" prefix1 <- "newDep5~" prefix1 <- "newDep55~" prefix1 <- "TCINRD~" prefix1 <- "TCINRD1~" set1 <- dplyr::select(raj_data, Sub.Sectors1, Size1, Year, Ownership1) set51 <- dplyr::select(raj_data, depthPartVertical) set52 <- dplyr::select(raj_data, comp11) set53 <- dplyr::select(raj_data, depthPartKIB) formula50 <- as.formula(paste(prefix1, paste(c(names(set1)), collapse = "+"))) formula51 <- as.formula(paste(prefix1, paste(c(names(set1), names(set51)), collapse = "+"))) formula52 <- as.formula(paste(prefix1, paste(c(names(set1), names(set52)), collapse = "+"))) formula53 <- as.formula(paste(prefix1, paste(c(names(set1), names(set53)), collapse = "+"))) fit50 <- glm(formula50, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit50)
fit51 <- glm(formula51,
312
family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit51)
fit52 <- glm(formula52, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit52)
fit53 <- glm(formula53, family=binomial(link='probit'), control = list(maxit = 500), data=raj_data1) summary(fit53)
texreg(list(fit50, fit51, fit52, fit53), digits = 3, caption.above = TRUE, booktabs = TRUE, dcolumn = TRUE, scalebox = 0.73, custom.model.names = c("(1)", "(2)", "(3)", "(4)"), single.row = TRUE, stars = c(0.01, 0.05, 0.1))
313
314