Gunay kazazoglu national innovation efficiency during the global crisis; a cross country analysis (2016)

In that context, this study underlines the role and the definition of the knowledge-based economy, its relationship to innovation, and national innovation efficiency by analyzing the inp

NATIONAL

INNOVATION EFFICIENCY DURING THE GLOBAL

CRISIS

A Cross-Country Analysis

Emine Nur Gunay and

Gozde Nur Kazazoglu

Global Crisis

National Innovation Efficiency During the

Global Crisis

A Cross-Country Analysis

ISBN 978-1-137-58254-6 ISBN 978-1-137-58255-3 (eBook)

DOI 10.1057/978-1-137-58255-3

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The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

My husband, Gokhan, who has been a constant source of support and encouragement during the challenges of my academic and political career and who has shared many uncertainties, challenges and sacrifices with me

throughout my career path;

The memory of my father, Dr Yusuf Cemal Ozkan, who has been my role model in my personal and political life for hard work, persistence, social

responsibility and personal sacrifices;

My mother, Nurcan Ozkan, who instilled in me the inspiration to set high

goals and who supported me with her prayers.

Prof Emine Nur Gunay-Ozkan

Gozde Nur Kazazoglu would like to dedicate this book to the following people:

My precious mother, Nergis Colak, who always supported me in times of adversity and helped me to gain self-confidence with her amazing courage,

dignified stance and power throughout her life;

My precious father, Aydın Kazazoglu, who always supported me in times of adversity and helped me to realize what life is in my early ages;

My lovely husband, Oguz Sahin, who always encouraged me about my capabilities and has been an amazing friend for years;

My grandfather, Dr Serif Kazazoglu, and my uncle, Prof Ali Rıza Kazazoglu, who always have been my role models in my education and

career path with their hard and continuous work.

Gozde Nur Kazazoglu Sahin

Globalization and the rise of the use of information and communication technologies (ICT) have shifted the comparative advantage of economies towards the factors of knowledge, innovation and technology Therefore, the knowledge-based economy plays a rather important role as far as sus-tainable growth and competitiveness enhancement are concerned On the other hand, the rise of a knowledge-based economy creates new challenges for business, policymakers and academia because the internal dynamics of the firms, sectors, countries and regions differ greatly Therefore, building

a unique innovation system based on a nation’s own dynamics is a real challenge for policymakers, especially during economic crisis

The Global Financial Crisis of 2007–2009, followed by the Euro Crisis, changed the world economic and social balance The effect of the global crisis on firms, sectors, countries and regions in terms of the intensity of the shock and the recovery process has also been uneven Some countries have emerged stronger from the crisis by making sound choices, imple-menting reforms and focusing their innovative energies on sustainable growth, while others have suffered severe economic contraction and high unemployment rates

In that context, this study underlines the role and the definition of the knowledge-based economy, its relationship to innovation, and national innovation efficiency by analyzing the inputs used and outputs created

in the 2000s, including during the period of the Global Financial Crisis The aim of this study is to trace the efficiency of national innovation sys-tems for 58 countries from 2000 to 2014, and to display the effects of the Global Financial Crisis on that efficiency by comparing and grouping

countries according to their GDP and GDP per capita This study makes comparison possible and thus this study will guide policymakers in devel-oping new policies by using the results of the comparison.

We wish to acknowledge Palgrave Macmillan and our editors Sarah Lawrence and Allison Neuburger for their sincere support and for main-taining high standards during the publication process We are also grateful

to the anonymous reviewers whose comments improved the manuscript enormously Last but not least, we thank the Bogazici University research Fund for their support of the project “Knowledge-Based Economy and Economic Development: A Cross-Country Analysis,” project code 7080

Uskudar Istanbul, Turkey Gozde Nur Kazazoglu Sahin

From Classical Production Functions to Knowledge-Based Economy 5

Definition of Innovation and National Innovation Systems 14

Indices Measuring Innovation and Knowledge-Based Economy 31

IT Industry Competitiveness Index (IT-CI) 42

European Innovation Union Scoreboard (IUS) 47

OECD Science, Technology and Industry Scoreboard (STI) 52

Conclusion 65

4 National Innovation Efficiency During the Global Financial

Advantages and Disadvantages of the DEA Model 73

Summary of Emprical Findings and Results 153

Development

IT-CI Information Technology Industry Competitiveness Index

Fig 2.3 Leger and Swaminathan’s model of innovation (Leger and

Fig 4.2 Constant and various returns to the scale efficiency frontier

Fig 4.4 Effects of random variation in the efficiency frontier

Table 2.1 Long waves and their phases identified by Kondratiev

Table 2.2 rothwell’s five generations of innovation model

Table 3.11 TAI grouping of countries according to their scores

Table 3.17 IUS categorization of countries (European Commission 2015) 51 Table 3.18 Global innovation growth rates and performances

Table 3.22 Global Competitiveness Index (WEF 2015) 57

Table 3.25 GII rankings (Cornell University et al 2015;

Cornell University et al 2013; INSEAD and

Table 3.26 Innovation efficiency rankings

Table 3.27 GII innovation efficiency ranking

Table 4.15 Base Model CCI 2000–2014 results according to

Table 4.16 Time-lag model CCI 2002–2014 results according to

Table 4.19 Average efficiency score for all models and for GDP grouping 130

Table 4.21 Number of efficient countries for all models and for

Table 4.24 Base model CCI 2000–2014 results for

Table 4.25 Time-lag model CCI 2002–2014 for

Table 4.26 Base model BCCI 2000–2014 results for

Table 4.27 Time-lag model BCCI 2002–2014 results for

Table 4.28 Average efficiency scores for all models and for

Table 4.29 Average efficiency for all countries and for

Table 4.30 Number of efficient countries for all models and for

Table 4.32 Overall efficiency change of the countries according to their

Table 4.33 Overall efficiency change of countries according to their

© The Author(s) 2016

E.N Gunay, G.N Kazazoglu, National Innovation Efficiency

During the Global Crisis, DOI 10.1057/978-1-137-58255-3_1

The Global Financial Crisis of 2007–2009, followed by the Euro Crisis, changed the world economic and social balance By turning upside down the internalized global, economic and social cycles and expectations, the Global Financial Crisis gave birth to a new world order and to a “new nor-mal.” Since the internal dynamics of firms, sectors, countries and regions differ widely, the effect of the global crisis on firms, sectors, countries and regions in terms of the intensity of the shock and the recovery process was also uneven

The average gross domestic product (GDP—in real terms) growth rate for advanced countries between 2000 and 2007 was 4.5 percent, while

in emerging and developing countries a 6.5 percent growth rate was observed on average After the financial crisis, between 2008 and 2014, advanced countries showed a 3.3 percent GDP growth rate, while emerg-ing and developing economies enjoyed a 5.5 percent GDP growth rate According to World Bank predictions, between 2015 and 2020 the aver-age GDP growth rate for advanced countries will be 3.7 percent and for emerging and developing markets 4.8 percent, meaning that although the gap between the two group’s GDP growth rates narrows, major countries, namely the G7 countries, will face a low GDP growth rate of only 1.96 percent on average in the next six years

There is enough evidence from historical and empirical research to demonstrate that, although some researchers argue that in times of crisis firms and countries tend to decrease investment in innovation, innovation

Introduction

drives growth in times of crisis and creates a fertile environment for those who turn the crisis into opportunity That is why adaptation of innova-tion is critical in times of crisis Using the indicators of the European Innovation Scoreboard 2009, a study across the European Union (EU) member countries indicates that countries endowed with stronger national innovation systems (NIS), such as Switzerland and the Nordic countries, have been less affected by and better able to respond to the recession, at least relative to new EU members (Filippetti and Archibugi 2010; Izsak

et al 2013)

Studies based on Europe have shown that despite the recent Euro Crisis, although research and innovation policies were protected after the crisis and even emphasized, funding levels have become difficult to sustain due to other structural and financial problems in the economic system According to the Organisation for Economic Co-operation and Development (OECD) Science, Technology and Industry Outlook of

2012, among 4238 European firms, a large share of countries decreased their spending on innovation and research and development (R&D) from

26 percent to 10 percent at the onset of the Global Financial Crisis pared to the pre-crisis period

com-Although Europe faced fundamental funding problems due to the high debt ratios in countries like Greece, Spain, Italy, Ireland and Iceland, OECD countries implemented recovery policies in innovation manage-ment to respond strongly to the financial constraints Public authorities recognized the relevance of human capital and skilled workers to knowl-edge management and supported educational institutions together with companies, especially small and medium-sized enterprises (SMEs) affected

by the lack of financial resources and credit access

Some countries in the OECD implemented strategies of smart specialization, which the OECD (2012b) defines as “the approach to combine industrial, educational and innovation policies to suggest that countries or regions identify and select a limited number of priority areas for knowledge-based investments, focusing on their strengths and comparative advantages” like Belgium, Canada, China, France, Hungary, Japan, the Netherlands, Portugal and the United States (OECD STI 2012a) Finally, structural measures to address weak-nesses in national innovation systems are beginning to be implemented, including “efforts to reform public research institutions in Italy and Greece, to enhance public–private collaboration projects in France, to

reduce red tape for business in Spain, and to work towards more pay-off for public spending on R&D and innovation in the United Kingdom” (OECD STI 2012a).

Empirical studies emphasize that national institutions play an especially important role in the strategic decision making processes of the economic agents and firms reacting to economic and market conditions such as labor markets, specialized sectors, industrial relationships, the educational system and the financial stability of a country (Freeman 1995; Hall and Soskice 2001; Nelson 2001; Coriat and Weinstein 2002; North 2005; Filippetti and Archibugi 2010) In addition, studies conducted on a meso level, which explain the enhancement of a firm’s competitiveness through innovation and knowledge management, argue that innovation increases a company’s survivability (Dosi 1988; Henderson and Clark 1990; Banbury and Mitchell 1995)

In that context, this study explains the role of a knowledge-based omy and its relationship to innovation and national innovation efficiency

econ-It does this by analyzing the inputs used and outputs created from 2000 to

2014 (by comparing and grouping countries according to their GDP and GDP per capita), a period that includes the Global Financial Crisis In the second chapter, various definitions and characteristics of knowledge and knowledge-based economies will first be explained in detail Definitions

of innovation will be provided and its evolution will be explained, and the importance of national innovation systems will be emphasized The third chapter will discuss the importance of measuring knowledge and the various methods of doing so; it will then discuss innovation efficiency and provide examples from indices used globally

In the fourth chapter, a review of the existing literature on measuring innovation efficiency will be provided together with a discussion of the methodology and the data used in the study Next, the Data Envelopment Analysis Model (DEA) will be introduced, measuring the innovation effi-ciency ratios of 58 countries after the crisis by using different assumptions and models of DEA with and without time lag assumption The final chap-ter presents policy recommendations and the conclusion

As a summary, this study provides an overview of the impacts of crisis

on innovation efficiency of countries and may serve as a course of action for policymakers by giving room for comparison with other countries included in the study and grouped according to GDP per capita and total GDP

BiBliography Banbury, Catherine M., and Will Mitchell 1995 The Effect of Introducing Important Incremental Innovations on Market Share and Business Survival

Strategic Management Journal 16: 161–182.

Coriat, Benjamin, and Olivier Weinstein 2002 Organizations, Firms and

Insti-tutions in the Generation of Innovation Research Policy 31(2): 273–290.

Dosi, Giovanni 1988 Sources, Procedures, and Microeconomic Effects on

Innovation Journal of Economic Literature 26: 1120–1171.

Filippetti, A., Archibugi, D 2010 Innovation in Times of Crisis: National Systems

of Innovation, Structure, and Demand Research Policy 40(2):179–192.

Freeman, Chris 1995 The National System of Innovation in Historical

Per-spective Cambridge Journal of Economics 19: 5–24.

Hall, Peter A., and David Soskice 2001 Varieties of Capitalism: The Institutional Foundations of Comparative Advantage Oxford: Oxford University Press.

Henderson, Rebecca, and Kim B.  Clark 1990 Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established

Firms Administrative Science 35: 9–30.

Izsak, Kincsö, et al 2013 The Impact of the Crisis on Research and Innovation Policies Study for the European Commission DG Research by Technopolis

innovation- union/pdf/expert-groups/ERIAB_pb-Impact_of_financial_crisis.

Nelson, R.R 2001 Making Sense of Institutions as a Factor Shaping Economic

Performance Journal of Economic Behaviour and Organization 44(1): 31–54 North, Douglas C 2005 Understanding the Process of Economic Change Prin-

ceton, NJ: Princeton University Press.

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oecd.org/sti/inno/smart-specialisation.pdf

© The Author(s) 2016

E.N Gunay, G.N Kazazoglu, National Innovation Efficiency

During the Global Crisis, DOI 10.1057/978-1-137-58255-3_2

With the rise of the use of information and communication technologies (ICT) and with the 2007 Global Financial Crisis, rather than an ortho-dox production and consumption economy, a new type of economy based

on the diffusion of know-how and technology gained in importance Based on economic and social welfare augmentation through the pillars of human capital, entrepreneurship, innovation, creation and the diffusion of technology, the knowledge-based economy (KBE) brings a breath of fresh air to the existing exogenous growth theories

The world economy has entered a period referred to as the tion age” with the diffusion of ICT technologies The shift away from traditional industry that started with the Industrial Revolution during the late eighteenth century has changed the appearance of the economic order Rather than the previously dominant Fordist production mode, which aimed at mass production based on an automation process involv-ing workers, in the so-called information age, the rise of information-intensive industries and high technology have engendered a greater need for sustainable competitive advantage This rise of information, technol-ogy and knowledge management have changed the dynamics of competi-tion in the market by shifting the framework of Porter’s generic strategies which used to be based on cost and product differentiation (Porter 1985) The shift has been away from mass production, in which a competitive

“informa-Defining Knowledge and the 

Knowledge- Based Economy

advantage was maintained based on cost leadership with a competitive edge of broader focus, to flexible production based on technology and knowledge, centered around differentiation, innovation and added value From individuals to countries, from the private sector to the public sector, all actors in the economy acknowledge that competitiveness is the key to success, and in order to maintain sustainable growth rates and to survive

in the market, one should not just adopt and adapt new technologies but also create value by merging knowledge and technology as a foundation

to create innovation and skilled human capital

The increasing significance of innovation and technology can be ered part of the rise of post-Fordism in the late twentieth century Rather than workers working on a simple and automated production line to sat-isfy the mass consumption habits of society, post-Fordism depends on the idea of small batch production, flexible specialization and the advantages

consid-of information technologies In addition, the flexibility and skill consid-of labor gained in importance, while sectorial clustering and the construction of industrial districts began to be seen

According to the Kondratiev (1935), the global economy, both cally and in the future, is characterized by long business cycles of fifty or more years These cycles, also called Kondratiev waves, were first accounted for on the basis of capital investment dynamics and later on technological innovations Kondratiev emphasized in 1935 that during the recession

histori-of long waves, an especially large number histori-of important discoveries and inventions in production techniques and communication are made, which are usually applied on a large scale only at the beginning of the next long upswing, just as the product life cycles Considering the long wave as the product life cycle, one can conclude that each long wave has its own birth, growth, maturity and decline sequence, and that at the end of the decline anomalies in the system arise and new middle range paradigm shifts occur for the economy or the sector (Levitt 1965; Day 1981; Kuhn 1962).The rise of post-Fordism and the long waves of Kondratiev reshaped Schumpeter’s idea of creative destruction In 1939, Schumpeter redefined the Kondratiev waves and argued that each business cycle was driven by technological innovation Creative destruction, for Schumpeter (1939), is the “process of industrial mutation and change creating a new economic structure while incessantly destroying the existing (old) one, thus always revolutionizing the economic structure from within.”

With this approach every Kondratiev wave is associated with a tain leading sector (or leading sectors), technological system or

technological style (Korotayev and Grinin 2012) Each wave represents and coincides with major economic or industrial turmoil, for example the third Kondratiev wave, which is sometimes characterized as “the age

of steel, electricity, and heavy engineering” coinciding with World War

I (see Table 2.1) The fourth wave, which coincides with world nomic growth, takes in the age of oil, the automobile and mass produc-tion Finally, the current fifth wave is described as the age of information and telecommunications (Papenhausen 2008), whereas the forthcom-ing sixth wave is sometimes predicted to be connected first of all with nano and biotechnologies (Lynch 2004; Dator 2006) Although each wave or technological change, considered as a middle range paradigm, shifts on a micro, meso or macro level depending on the dynamics of the change itself, David and Foray (2003) consider these changes, or the move to a new knowledge or to a more knowledge-based economy, as a

eco-“sea change” or “soft discontinuity” rather than a sharp discontinuity or break from the previous structure of the economy

Assuming that the driving force of economic growth is knowledge rather than capital accumulation, the newly trending Neo-Schumpeterian economics puts a strong emphasis on knowledge, innovation and entre-preneurship at the micro level (individual) and also underlines the impor-tance of Marshall’s conception of meso-economics, which takes place between the macro and the micro levels of economic analysis (Hanusch and Pyka 2007) As previously underlined shift in Porter’s generic strate-

Table 2.1 Long waves and their phases identified by Kondratiev (Korotayev and

Tsirel 2010; Grinin et al 2012)

Long wave

number Long wave phase Beginning dates Ending dates Period

gies of maintaining competitive advantage and Neo-Schumpeterian nomics evolved, competition based on innovation takes the place of price competition Although pricing strategies are still considered significant, they are not central if one considers the driving forces of economic devel-opment Since in economic reality, every process cycle has an end, innova-tion is the key factor responsible for setting up new process circles (which can be classified as product life cycles or middle range paradigm shifts) and limiting conditions, whereas prices are responsible only for limiting the existing conditions (Hanusch and Pyka 2007).

eco-Neo-Schumpeterian economics deals with the uncertainty of the future with three main pillars or actors in the economy that constitute the Triple Helix, which is formed by universities, industry and public sector/gov-ernment Industry, finance and the public sector are the dominoes of the economic system in Neo-Schumpeterian economics; one mistake by any

of them can hinder the development of the whole economic system That

is why, according to the Neo-Schumpeterian approach, not only nological innovations but also institutional, organizational, social and political innovations are considered the key pillars in a knowledge- based economy (Hanusch and Pyka 2007) This macro perspective of Neo-Schumpeterians brings up new questions to answer and new problems to solve: What is knowledge? What is a knowledge-based economy? What does a knowledge- based economy contain? Who are the actors and what are their roles in a knowledge-based economy?

Friedman, in 2005, stated that the world is getting flatter—especially since the 1980s—as a result of the globalization According to Friedman, there are ten flatteners that he conceptualized as milestones in the global playing field and each of them started a new era for world economic and political stability They are:

1 Collapse of the Berlin Wall in 1989

2 Netscape going public in 1995

3 Workflow software that links machines together without humans

4 Uploading of information

5 Outsourcing services and manufacturing

6 Offshoring to increase competitiveness

7 Increasing supply-chain efficiency, as Wal-Mart did

8 Insourcing between companies

9 Better informing and increasing the number of search engines

10 The steroids, which include wireless, voice over Internet, and file sharing

Friedman and Mandelbaum (2011) argued that with increased use of ICT, the world has moved from being flat to the stage of ‘hyperconnectiv-ity’ In this new stage, people do not just easily access any kind of infor-mation they desire— they also create their own knowledge by continuous production and distribution through technology This rapid transforma-tion from an industrial economy to a knowledge economy brings some key definitions to light

Fritz Machlup, as the first economist to identify knowledge as an nomic resource, describes knowledge as a commodity and information source (Boettke 2002) For Machlup, information is knowledge only if

eco-it is communicated and used; knowledge weco-ithout communication or semination is just information To remark the difference between infor-mation and knowledge, Machlup (1984) argues that “the semanticist will note that the verbs ‘to inform’ and ‘to know’ have different meanings: informing is a process or activity, whereas knowing is a state of mind … on the other hand, both nouns are used also for the contents (the sense, not the size) of what people know or are being informed about.”

dis-Machlup’s definition includes all kinds of knowledge such as scientific, daily and religious knowledge (Godin 2008) Distinguishing five types of knowledge, Machlup (1962) categorizes knowledge as practical knowl-edge, intellectual knowledge, pastime knowledge, spiritual knowledge and accidentally acquired knowledge Practical knowledge is that gathered from businesses, professionals and daily activities, which can be useful in later decision-making processes, it is the knowledge gathered accidentally

or unconsciously Spiritual knowledge corresponds to religious knowledge and the salvation of the soul Pastime knowledge corresponds with daily knowledge created by satisfying the need of entertainment and through curiosity, which are nonintellectual factors; it maintains the “passive relax-ation from serious pursuits apt to dull his sensitiveness” (Wallace 2007) Finally, intellectual knowledge corresponds to intellectual curiosity about any topic, which can be gathered by schooling, education or any scientific research

On the other hand, Demarest (1997) defines knowledge in a more economical way and on a more meso level/firm specific as “the actionable

information embodied in the set of work practices, theories-in-action, skills, equipment, processes and heuristics of the firm’s employees.” Drawing attention to commercial knowledge, Demarest underlines the importance of dissemination and communication in knowledge manage-ment, as Machlup did by emphasizing that distribution of knowledge is the only way to increase company performance and efficiency and thus

to create competitive advantage (1997) It is important to note though, that although definitions of knowledge can vary, the belief that knowledge increases with sharing and making it accessible for all never changes; thus knowledge requires inclusiveness for all

Quah (1999) defines the main characteristics of knowledge and edge products by explaining three different properties, which are uncer-tainty, superstar dynamics and infinite expansibility Arguing that the production of knowledge and knowledge products are uncertain, Quah emphasizes a very important issue in today’s economic and technological growth: an increase in knowledge input does not automatically lead to

knowl-an increase in the output The effect of one unit of increase in the input may lead to an increase, a decrease or does not change the output This uncertainty in the creation of output, meaning knowledge production,

is the issue of efficiency, which tries to maximize the output with a given level of input or to minimize the input for producing a given level of out-put For the invention and implementation process (for example one can think of the commercialization process of a piece of knowledge by taking

a patent), Quah newly defines the term ‘superstar dynamics’, referring to the idea of the ‘first/winner takes all’ characteristics of the knowledge Although the invention process is risky in terms of whether it will be suc-cessful or not, if one manages to succeed as an inventor of knowledge then the product could take the all from the market The last property underlines the ‘infinite expansibility’ of knowledge, meaning that knowl-edge is a lasting product which multiplies with an increasing number of users This expansibility of knowledge is also underlined in the context of dissemination of knowledge and how information evolves into knowledge with continuous and open sharing Agreeing with Quah, Houghtan and Sheehan (2000) and Stiglitz (1999) emphasize that once knowledge is made public, expansiveness arrives with more users In addition, Stiglitz (1999) highlights that once the knowledge becomes a pure public good,

it is hard to prevent public usage

According to Warsh (2006) and Foray (2006), intellectual erty rights can be one solution to the non-excludability of knowledge

prop-Foray  (2006) argues that intellectual property rights are important

in terms of granting temporary exclusive rights to the inventor so that there will be motivation for the inventor/producer for further research and development The main economic and social challenge here arises from the non- excludability and dissemination of knowledge Designing

an effective patents system is a way to maintain a balance between the social objectives and benefits of ensuring efficient use of knowledge, and its dissemination once it has been produced, and also providing necessary outcomes and benefits for the inventor This lack of a patents system and market efficiency, as will be seen in the next chapter, affects the over-all research and development structure and effectiveness of the complete cycle, including all actors in the economy In addition to the definitions and traits discussed so far, by defining knowledge as “the ultimate eco-nomic renewable”, Brinkley (2006) indicates that the stock of knowledge

is not depleted by use

Since, as it is discussed so far, there is no agreed, a one-size fits all definition of knowledge, the extended definition of a knowledge econ-omy varies across institutions and actors The Organization for Economic Co-operation and Development (OECD 1996) defines a knowledge economy as an economy which is directly based on the production, dis-tribution and use of knowledge and information Powell and Snellman (2004) describe a knowledge economy as “production and services based

on knowledge-intensive activities that contribute to an accelerated pace of technological and scientific advance as well as equally rapid obsolescence The key components of a knowledge economy include a greater reliance

on intellectual capabilities than on physical inputs or natural resources.” Smith (2002) argues that the OECD’s definition of a knowledge economy

is a good example of the problem with the term, since every economy

is based on knowledge to a certain degree and it is hard to differentiate which one is directly based on knowledge

Going even further, Quah (1999) defines to knowledge-based economy

as weightless economy, referring to the four categories in KBE, which are ICT, all kinds of intellectual property including patents and utility models etc., electronic databases and clouds, and biotechnology Since the value added to GDP has little physical contribution from increased use of tech-nology, it is once more emphasized through definition that knowledge is not a traditional input that creates traditional and physical products.The Economic and Social Research Council (2005) describes a knowl-edge economy “as the effective utilization of intangible assets such

as knowledge, skills and innovative potential as the key resource for competitive advantage.” Aside from these definitions, the United Nations portrays a knowledge economy as one where the production, distribution and use of knowledge maintain growth, wealth creation and employment

in the overall economy

In addition, the World Bank (WB) defines a knowledge economy as

“one where organizations and people acquire, create, disseminate, and use knowledge more effectively for greater economic and social development” (2014) The WB also defines four key pillars that help countries articulate strategies for their transition to knowledge a economy from factor-driven and efficiency-driven economies (World Bank 2014):

1 “An economic and institutional regime that provides incentives for the efficient use of existing and new knowledge and the flourishing of entrepreneurship

2 Educated and skilled populations that can create, share, and use edge well

3 An efficient innovation system of firms, research centers, universities, think tanks, consultants, and other organizations that can tap into the growing stock of global knowledge, assimilate and adapt it to local needs, and create new technology

4 Information and Communication Technologies (ICT) that can tate the effective communication, dissemination, and processing of information.”

facili-This coordination and cooperation among the pillars that the WB underlined can be included in the scope of Neo-Schumpeterian econom-ics, since Neo-Schumpeterian theory concerns not only the transforma-tion or shifts in the technological area but also the public and monetary side of the economy together with socioeconomic effects As discussed previously, industry, finance and the public sector are dominoes of the economic system in Neo-Schumpeterian economics; one mistake by any

of them can hinder the development of the whole economic system In order to minimize the failures of the system, the whole economic sys-tem and all its actors should work in tandem with each other and use a given set of priorities, strategies and distribution of work so that ineffi-cient use of scarce resources and time can be prevented, or at least mini-mized This organic system that works in harmony is called a national innovation system, which will be discussed further in the chapter

In addition to the WB, the OECD emphasizes the need for coordination and cooperation between actors in the market to maintain high rates of knowledge diffusion By differentiating between tacit knowledge and codi-fied knowledge, the OECD underlines the growing importance of highly skilled workers related to increasing demand in high-tech goods and knowl-edge-intensive industries In order to emphasize the importance of tacit and codified knowledge, Van den Berg (2001) notes that the quality of the labor force, its accumulated experience and its education system determine

an economy’s ability to create new ideas and adapt old technology

Smith (2002) also criticizes Drucker’s and the OECD’s claims about the increasing importance of knowledge Drucker (1993) stresses the impor-tance of human capital as a carrier of knowledge, stating that knowledge

is “the real and controlling resource and the absolutely decisive factor of production” while the OECD (1996) suggests that the role of knowledge (as compared to other factors of production such as capital and labor) has taken on greater importance Smith (2002) examines Drucker’s argu-ments that ‘knowledge is sidelining capital’ by propounding that separat-ing knowledge accumulation from capital accumulation is impossible and stating that knowledge cannot be converted into a product without capital investment This point can be discussed under the topic of entrepreneurship and investor relationships, since what Smith advocated about investment is extremely important in developing countries where entrepreneurs or small- and medium-sized enterprises face difficulties in obtaining grants, angel investors or any type of capital needed for further research or production.The OECD, on the other hand, underlines the importance of knowl-edge by arguing that the definition of knowledge is broader than that of information It categorizes knowledge into four groups where the first two correspond with codified knowledge and the last two tacit knowledge (1996):

1 Know-what refers to the facts that are known by everybody and are written

2 Know-why refers to scientific knowledge of nature’s laws and the cause-and-effect relationship of scientific incidents

3 Know-how refers to skills and capability of doing something It is ally learnt and developed within the actor and not shared generally

4 Know-who refers to the information of knowing who does what This has an increasing importance in the mounting need for efficiency in the supply chain mechanism

While measuring know-what and know-why can easily be used in classical production functions, know-how and know-who are more diffi-cult to codify and measure without conducting a measurement framework since those kinds of knowledge can be transmitted by either formal or informal channels of information This difficulty in measuring tacit knowl-edge is seen in reports comparing the knowledge bases of economies because there are no specific indicators which fully reflect the total amount

of tacit knowledge and its positive effects in increasing overall wealth

Adam Smith (1776) in his The Wealth of Nations revealed that the wealth

and prosperity of nations depends heavily on the productive powers of labor in addition to capital This remarkable point later turned into the notion of innovation, which is shaped by the codified and tacit knowledge

of the worker

Referring to innovation as any kind of new combination, Schumpeter (1939) defines it as “setting up a new production function” that creates disequilibrium and carries the existing economic system to a new station-ary situation of equilibrium Rather than a shift along the production func-tion frontier, Schumpeter’s definition of innovation refers to the economic notion of a shift of the production function itself (Hagedoorn 1996).Rather than the technological innovations that Schumpeter termed

“creative destruction”, in today’s world, any kind of change that ates a new added value to the existing economic system is considered innovation For example, for the OECD, innovation is the implemen-tation of a new or significantly improved product (good or service) or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations (OECD Oslo Manual 2005) Meanwhile Van de Ven (2008) defines innovation

cre-as an idea that is perceived cre-as new to the people involved According

to Van de Ven, even though it may appear to be an imitation of thing that exists elsewhere, something can be called an innovation if it

some-is new to the people who benefit from it Thsome-is some-is the point of creation

of a new or additional value by integrating innovation or technological change into existing systems As long as the knowledge is shared, there can be creation of value for potential users Even though the knowledge

is not new, the way it is used or applied can generate fresh benefits for individuals, firms and countries

Taking the OECD definition of innovation (as explained above) as the basis, all of the policy recommendations for sustainable economic growth and development underline the role of innovation, and the sus-tainable and efficient investment in knowledge creation The use of this fresh knowledge for the transformation of the old economic system into

a knowledge- based economic system is programmed to create continual economic value

Freeman (1995) argued that the rates of technical change and nomic growth depend on efficient use of resources and innovation effi-ciency, rather than investing high ratios of gross domestic product in research and development or being the first in the world with radical innovations This is the problem of innovation management, especially after the global financial crisis, which once more reminded policy-makers and firms that the classical sources of production are scarce This is also how middle-income countries such as Japan and South Korea avoided the middle income trap by strategically focusing on sector-based policies and investments in certain research areas In addition, Freeman emphasizes that as much as technical innovation, social innovations play a significant role in economic growth and development (1995) This rising significance

eco-of social innovation can even include changes in the way eco-of doing business

in the mindset of the public

With 2000s, rather than excessive R&D spending in every sector, national and regional innovation systems and strategies arose which give necessary importance to the concept of the Triple Helix and factor endow-ments of countries and regions in order to maximize their competitive advantage So as to understand the notion of a national innovation system,

it is crucial to grasp the evolution of innovation’s definition and the rence of endogenous growth theory

occur-Evolution of Innovation

One of the first (theoretical) frameworks developed for historically standing science and technology and its relation to the economy has been the “linear model of innovation” (Godin 2005) Starting with basic research, the innovation process continues with applied research and devel-opment on a subject and finally ends with the creation of economic value, which is the production and diffusion of the product (good or service) (see Fig 2.1) This is a vicious and simplified circle or the simplest way of modeling the process of innovation, which can be considered as a closed system to the exogenous variables

under-In the linear model of innovation, innovation occurs due to two factors

or conditions, which are demand pull or technology push In the former approach, the market conditions or the consumers’ demands are the cata-lysts of innovation In the latter approach, technology and investment in R&D are the basis for the innovation process

Mensch (1979) showed that the feedbacks from consumers or other producers and users in the process of innovation may deter linear repre-sentations of innovation He gave the example of computer lifecycles in the UK during the 1960s by emphasizing that the users’ feedback may affect the linearity of the innovation process and this linearity may thus turn into a more complex process This example is also important in terms

of transformation of existing production dynamics into a more user or consumer dominant logic from a product-oriented view

Kline and Rosenberg (1986) came up with a new formation of tion called the chain-linked model of innovation, which focuses on uncer-tainty and complexity in the innovation process (see Fig 2.2) Contrary

innova-to the linear model of innovation, Kline and Rosenberg argue that new knowledge is not necessarily needed for innovation With complex feed-back loops between all stages in the process and due to product lifecycles, the uncertainty in the market’s reaction to the innovation may require redesigning and reproducing without any basic research, as indicated

in the linear model of innovation The chain link model of innovation highlights that for innovation to continue new knowledge is not always a required input, as Van de Ven (2008) also recognized new ways of benefit-ing or new value creation from already existing knowledge

Fig 2.1 Linear model of innovation (Godin 2005)

In 1992, Rothwell created his famous five generations of innovation models By giving a historical overview of innovation models, Rothwell classifies the transformation of innovation models into five genera-tions as: technology push, demand pull, coupling model, integrated model and a systems integration and extensive networking model (see Table 2.2).

Leger and Swaminathan (2007) reconsider the chain link model of innovation and create their own model by introducing four new aspects

in the firm level:

Fig 2.2 Chain link model of innovation (Kline and Rosenberg 1986)

1 There is always a need to appropriate the returns from innovation by maintaining intellectual property rights to continue the motivation for further research.

2 There are two types of knowledge in the sector First, an industry- based knowledge, which is available to all other firms in the market and created

by all firms in the market This industry-based knowledge is shared and open to all firms and future new entrants, which can be generated by spillovers The second type of the knowledge is tacit knowledge, which

is firm-specific and not shared and open This tacit knowledge is the main way for a firm to maintain competitive advantage

3 The environment in which the firm exists also has significance, as Porter’s Five Forces shows (2008) by grouping environmental forces into five—the power of buyers, power of suppliers, threat of substi-tutes, threat of new entrants and the existing rivalry in the market.Possibility of new entrants, threat of substitutes and existing rivalry in the market can imply the high risk of a possibility of imitation or the first-takes- all concept of superstar dynamics of knowledge creation That

is why regulatory mechanisms, which are appropriation mechanisms, play

a crucial role in terms of defining the borders of each firm and their ities that could be legally detrimental to other firms and in protecting the intellectual rights of firms This competitive rivalry and threat of new entrants, together with the threat of substitutes, generates a highly impor-tant innovation capability and market structure relationship Williamson (1965) emphasizes that the relative share of innovation contributed by the largest firms in the industry decreases as the monopoly power, which is the concentration ratio, increases That is why maintaining a fair competitive environment through appropriation mechanisms and legal structures pro-tecting both the consumers and firms brings forth positive externalities in terms of innovation and knowledge creation

activ-Table 2.2 Rothwell’s five generations of innovation model (Kutvenon 2007)

Generation Key features

elements

response, continuous innovation

4 The last aspect is the characteristics of the firm, meaning the size, resources, investment in R&D, employee education and firm-specific factors, which constitutes the competitive advantage of the firm if uti-lized and evaluated effectively.

By introducing these four new aspects, they divide the general notion

of ‘knowledge’ in a chain link model of innovation into subcategories

of information: industry information and firm-specific knowledge (see Fig 2.3)

These different approaches of innovation processes can also be linked

to approaches of growth theories, which are the exogenous growth model

of Solow and the endogenous growth model of Romer

In Solow’s neoclassical growth model, the source of the growth, which is technological progress, is left unexplained, whereas Romer fol-lows Arrow’s seminal work on the economics of learning by doing Arrow noted from case studies that there was strong evidence that experience and increasing productivity were associated (Ickes 1996)

Fig 2.3 Leger and Swaminathan’s model of innovation (Leger and Swami -

Romer (1986), by emphasizing the notion of ‘learning by doing’, which

is also the knowledge stock specific to each country investigated, also gives significance to the concept of ‘country-specific factors’ Country-specific factors are always considered as the initial factors affecting the innova-tion capacity of countries National innovation capacity, which is com-posed of the country-specific factors, is the ability of a country to produce and to commercialize a flow of innovative technology over the long term (Furman et al 2002) This ability of the country depends on environmen-tal factors that the country had, has and will have

According to evolutionary economics, the environment that the firms, markets, institutions and other actors operating in the economy exist within should be taken into consideration while analyzing the transformation pro-cess of countries The cooperation and coordination of the actors, the inter-industry,inter-governmental and even geographical differences constitute the national innovation systems of countries, which are crucial in the way each country’s growth rates converge and the way innovation diffuses

By growing out the linear model of innovation processes, a new interactive and modern model of innovation processes gained acceptance with the rise of Neo-Schumpeterian economics and changes in the environmental factors distorting the linearity of the processes with back and forth knowl-edge and feedback transfer National Innovation Systems (NIS) discussed and explained by Freeman (1995), Lundvall (1992) and Nelson (1993) can be briefly summarized as the relationship or the network between the actors in the whole economy or the recently defined Triple Helix Freeman (1995) defines NIS as “the network of institutions in the pub-lic and private sectors whose activities and interactions initiate, import, modify and diffuse new technologies,” while Lundvall (1992) describes

it as “the elements and relationships which interact in the production, diffusion and use of new, and economically useful, knowledge.” Nelson (1993) portrays it as “a set of institutions whose interactions determine the innovative performance of national firms.”

Pavitt and Pavel (1994) show that the rate and direction of cal learning of a nation is determined by the national institutions and their incentives, while Metcalfe (1995) includes not just the national institu-tions but all actors in the economy, as their joint or individual activities contribute to the development and diffusion of existing and new technol-

technologi-ogies That is why, for an national innovation efficiency system, the whole system should be working in harmony towards a predetermined strategic context and goals The notion of a ‘flat world’ and ‘hyperconnectivity’ can be adapted to the national level, since national innovation systems flatten the differences between nations and increase interconnectedness (Friedman and Mandelbaum 2011).

The smooth operation of innovation systems depends on the fluidity of knowledge flows (open and shared knowledge which is non- excludable)—among enterprises, universities, research institutions and also governmental bodies (OECD 1997) The importance of tacit knowledge and know-how exchanged through informal channels, and the sharing codified knowledge

or intellectual knowledge in publications, patents and other formal sources are undeniable since knowledge grows with the increase in the number of people using it, which is the multiplier effect of knowledge (see Fig 2.4) The mechanisms for knowledge flows include four main categorizations, which are joint industry research, public/private sector partnerships, tech-nology diffusion and movement of personnel (OECD 1997)

It is also important to remember that the dynamics, properties and the efficiency of the national innovation systems will be different between devel-oped and developing countries (Arocena and Sutz 2000; Bartels et al 2012;

Gu 1999; Intarakumnerd et al 2002) The NIS in developing countries is called “ex-ante systems” in comparison to developed countries’ “ex-post sys-

Interactions among

entreprises

Diffusion of knowledge and technology to firms

tems” (Arocena and Sutz 2000; Gu 1999) The differences between being

“ex-ante” and “ex-post” emanate from the difference between the already established and working institutional systems in developed countries and the unsettled mindset and inefficient institutional systems in developing countries Particularly in developing countries, the cultural way of doing business and the embedded autonomy of the government can be shown as being among the crucial factors affecting the implementation of national innovation strate-gies In addition, it is important to note that until the Global Financial Crisis,

“unlike developed countries, capital accumulation, rather than intangible assets (such as knowledge) and learning, is the main contribution to technical prog-ress in developing countries” (Gu 1999)

After the crisis, developing countries with more settled economic ditions relative to developed countries, achieved high growth rates and enjoyed higher R&D expenditures as a ratio of GDP. According to the

con-WB statistics, high-income countries invested 2.3 % of their GDP in R&D

in 2006, while the ratio remained 2.3  % in 2013 On the other hand, middle-income countries increased their R&D expenditure from 0.9 % of GDP in 2006 to 1.3 % of their GDP in 2012 Although a higher budget for research activities is perceived as an important and affirmative indicator both in short-term and long-term strategy formulation in the literature about innovation and knowledge-based economies, more importantly the effectiveness of the way the budget is spent and the outputs should be observed and tracked continuously

National innovation systems are crucial for nations on their way to determining short and long-term national and regional innovation strategies As one-size-fits-all, innovation strategies do not work for each and every country; policymakers should be aware of specific national and regional factors and build the Triple Helix model based on these specific competitive areas, which, ultimately, help a country to win a competi-tive advantage Chen et al (2011) reveal the importance of an innovation environment by suggesting that it affects the efficiency and productiv-ity of the innovation process for all kinds of decision-making unit in the economy

Porter (1990) underlines that competition in today’s world is more dynamic than in the days of classical production functions, and that nations with the economic goal of producing a high and rising standard of living for their citizens should increase their ability to maintain an efficient productivity This efficient productivity relies on competitive advantage, which rests on continual innovation Thus, it is crucial to compare the innovation efficiency of nations in order to better implement sound poli-

cies for increasing the efficient use of scarce resources and to understand the deficiencies and adapt models of success to the specific dynamics of each economic and social system and their resources.

Since these terms have played a significant role in sustainable nomic growth and social welfare since the 2007 financial crisis, policies implemented by countries at the regional or national level to maintain growth and welfare contain strategic arguments about innovation and the knowledge-based economy and their implications for realizing the goals set for the short term, medium term and long term In order to set these goals and implement the necessary policies, countries should be able to measure their input and how efficiently they turn this input into value- added output By bearing this question in mind, the next chapter explains the way these inputs, outputs and the efficiency of innovation systems are measured by analyzing the recent performances of countries and their rankings constituted by the indices designed and used with a chosen set of indicators by global institutions

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