Measuring Innovation Efficacy: An Operational Framework ... · the underlying conceptual thinking behind most innovation measurement tools, which is the linear model of innovation

Measuring Innovation Efficacy:

An Operational Framework for Mapping

and Measuring Innovation Capacity and

Performance of Countries

_______________

Sami MAHROUM

Yasser ALSALEH

2012/05/IIPI

Measuring Innovation Efficacy: An Operational Framework for Mapping

and Measuring Innovation Capacity and Performance of Countries

Sami Mahroum*

Yasser Alsaleh**

Acknowledgements

The author would like to thank the Department of Economic Development, Abu Dhabi, for financially

supporting this research project. Thanks are also extended to the Abu Dhabi Education Council, which has

supported the work of INSEAD Innovation and Policy Initiative.

.

* Director, INSEAD Innovation and Policy Initiative at INSEAD Abu Dhabi Campus, P.O. Box

48049, United Arab Emirates. Email: [email protected]

** Senior Research Fellow, INSEAD Innovation and Policy Initiative at INSEAD Abu Dhabi

Campus, P.O. Box 48049, United Arab Emirates. Email: [email protected]

This working paper was developed using funds made available through the Abu Dhabi Education

Council, whose support is gratefully acknowledged.

A Working Paper is the author’s intellectual property. It is intended as a means to promote research to

interested readers. Its content should not be copied or hosted on any server without written permission

from [email protected]

Find more INSEAD papers at http://www.insead.edu/facultyresearch/research/search_papers.cfm

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Abstract:

In an increasingly globalised economy, the ability to draw in innovations and ideas from elsewhere

and build on them to create value at home has become a powerful facility for economic growth.

Since some places are better at adopting and adapting borrowed ideas than others, the process of

‘innovation through adoption’ deserves more attention at both scholarly and policymaking levels.

Based on such beliefs, this paper elaborates the notion of ‘innovation adoption’ and develops it

further to advance the notion of ‘innovation efficacy’. The latter is interpreted here as the efficiency

and effectiveness of innovation systems in terms of accessing, anchoring, diffusing, creating and

exploiting innovations. This notion is further illustrated in a measurement tool based on a composite

index, which we name the ‘Innovation Efficacy Index’.

The ultimate contribution of the paper lies in its aim to shift the traditional focus of attention from a

fixation with developing and exploiting new knowledge locally to the prospect of value creation

through accessing, anchoring or diffusing knowledge acquired from elsewhere.

Keywords: Innovation Index; Functions of Innovation Systems; Composite Indicators

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1. Introduction

Innovation has become a policy priority in many countries supported by national strategies and large

budgets. Subsequently, innovation has taken on a more central role and many governments have

established dedicated ministries, departments and offices to support the study, incorporation and

implementation of innovation policy. Thus, in order to evaluate the effectiveness of governments’

intervention, various innovation indices have been developed over the years to measure innovation

performance at the national and sub-national levels. The most prominent among these indices

appear to be the European Union’s Innovation Scoreboard (PRO INNO Europe, 2009); the OECD

Science, Technology, and Industry Outlook (OECD, 2010a); the Nordic Innovation Monitor (Norden,

2009); as well as indices developed by UNCTAD (e.g. UNCTAD, 2005) and the World Bank (2010).

To date, innovation indices and subsequently policies have tended to focus on two main aspects of

innovation activities: creating new knowledge, and exploiting new knowledge and innovations

(NESTA, 2007). With this focus, they have systematically missed the real interest of policymakers and

tax payers in innovation, namely, a tool for value-creation and problem-solving. This is largely due to

the underlying conceptual thinking behind most innovation measurement tools, which is the linear

model of innovation (NESTA, 2007). The linear model of innovation assumes a uni-directional

relationship between various inputs and outputs in the innovation process, such as expenditure on

research and development (R&D) and product development, or science and engineering (S&E)

enrolment and S&E performance (OECD, 2002).

As a result, traditionally, innovation measurement tools have paid little attention to the importance

of what happens in between input and outputs (i.e. throughputs), more specifically the learning,

adoption and adaptation of knowledge that takes place within the process of innovation. These

essential elements of the innovation process often go un-accounted for in the traditional innovation

measurement tools despite the often cited paradoxes of countries making modest investments in

innovation inputs still enjoy high innovation outputs (for example, in Australia, Belgium, Canada,

Norway and the United Kingdom ‘UK’) (Andersson & Mahroum, 2008).

Landry and Amara (2010) noted that policymakers often focus on implementing innovation policies

that are largely based on one or two measurement components, such as patents and R&D

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investment to attain a certain policy objective. Likewise, in Archibugi and Coco (2004) and Archibugi

et al. (2009) it was argued that many traditional innovation indicators, such as those focused on

science and technology activities, are not helpful for measuring innovation in developing countries

as they do not reflect the multifaceted factors that contribute to innovative capacities in different

countries. To illustrate further this point, consider governments’ classical emphasis on the role of

investment in science, technology and education on economic growth. For example, whilst one

country may invest in education and see its investment translate into tangible economic growth,

another may invest just as much and see little in return. In a similar vein, empirical evidence

suggests that higher enrolment in higher education does not necessarily translate into the higher

economic development of a country (e.g. consider the cases of Switzerland and Korea in Figure 1).

Figure 1: In search of a relationship between enrolment in higher education and economic

development (Source of Data: World Bank, 2009)

As Balzat (2003) put it “Since even the linkage between R&D spending and innovative success is

far from being fully understood or easily measurable, it appears thus to be far-fetched to

establish a direct tie between innovative input and real economic performance” (pg. 9). Thus,

whilst learning from international experience could be an instructive exercise, it must be borne in

mind that similar inputs made by different countries may not yield similar outputs (Chang, 2010).

Thus, benchmarking the performance of innovation systems that are inherently different cannot be

used for identifying best practice, but rather as a platform for generating systemic comparisons with

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context-specific insights (Balzat, 2003). Understanding such cross-country differences requires the

adoption of a holistic view of the innovation capabilities of nations (Fagerberg and Srholec, 2008),

which this paper aims to capture.

In this paper, we introduce a measurement tool (namely the Innovation Efficacy Index), which is

based on a more nuanced model of the innovation process that takes into account various

mechanisms of learning, adopting and adapting that lead to different types of innovation outputs.

Having introduced the context of this work, the rest of the paper is organised as follows: Firstly, we

briefly discuss why governments, policymakers and analysts seek to measure innovation activity.

Secondly, we discuss current state-of-the-art conceptual frameworks that help explain how

innovation works at the level of the national economy. Thirdly, we introduce our own model (namely

the AC/DC model), which provides the theoretical foundation for the Innovation Efficacy Index (IEI).

Towards the end of the paper, we operationalise the IEI using snapshot datasets in order to show

how it could be used to generate meaningful policy analysis and recommendations.

2. Why do governments bother with measuring innovation?

It is now well recognised by most governments that innovation is a key driver of economic

development and a fundamental source of competiveness in the global marketplace (OECD, 2010b).

This has been supported by considerable evidence – both theoretical (e.g. Romer, 1986; Solow,

1956) and empirical (Mani, 2002; Mansfield, 1972; Nadiri, 1993) – that suggests that innovation

plays an important role in achieving economic growth. A fundamental reason for governments’

interest in measuring innovation is that they are often called upon to develop policies and adopt

measures to foster innovation activity. Government intervention in the innovation activities domain

stems from the belief that many of the elements of the innovation process are either public good

(such as education and infrastructure) or political in nature (such as legislations and regulations).

Like other facets of government work, innovation policy necessitates the development of

measurement tools that assist governments in both developing, and evaluating the effectiveness of,

policy interventions (Earl and Gault, 2006; Smith, 2005). As a result of the increasing demand for

developing sound evidence base for policy (Sanderson, 2002), the use of research-based insights has

become embedded within day-to-day policymaking around the world (Campbell et al., 2007).

In essence, making effective innovation policies underscores the ability of a given nation to measure

its innovation capabilities (NESTA, 2009). In this regard, Patel and Pavitt (1994a), among other

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scholars, have emphasized the importance of constructing, and using, frameworks to measure

national innovative performance in order to provide inputs for innovation policy.

3. The efficacy of innovation systems as an analytical framework for

measuring innovation

The most prevalent conceptual framework for understanding innovation processes at the level of

the economy is the concept of Systems of Innovation (SI). The SI concept originated in the work of

Bengt-Åke Lundvall and Christopher Freeman in the late 1980s. Drawing on the notion of ‘The

National System of Political Economy’, which was originally articulated by Friedrich List (1789-1864),

Freeman (1987) provided a historical account of the rise of Japan as an economic superpower.

Around the same time, Lundvall (1988) highlighted the importance of adopting a systemic view

through studying social interactions between suppliers and customers and their role in stimulating

innovation.

The SI concept was subsequently further developed both conceptually and empirically by a large

number of scholars, most notably by Lundvall (1992), Nelson (1993) and the OECD (1997). Whilst not

being a theory per se, the SI is a theoretically-rich conceptual framework that has recently gained a

lot of attention in the field of innovation studies (Edquist, 2005). In spite of such popularisation, the

SI concept is still considered as an emerging analytical framework whose definition varies

considerably, depending on the characteristics of the system being considered. However, the most

cited definition appears to be that of Lundvall (1992), who broadly defined the SI concept as “*t+he

elements and relationships which interact in the production, diffusion and use of new, and

economically useful knowledge” (pg. 11). Alternative definitions of the concept are provided by

other SI scholars (including Edquist, 2005; Freeman, 1987; Metcalfe, 1995; Patel and Pavitt, 1994b).

The most important message in SI concept is that actors do not, and cannot, innovate in isolation

and hence innovation is a collective and interactive process. Additionally, the SI approach has

changed the analytical perspective on innovation from the traditional linear models (e.g. that of a

‘technology push’ and ‘market pull’ or of ‘basic research – applied research – development –

diffusion’) to a systemic view of interaction among different actors (Edquist, 2005). Adopting an SI

perspective has the potential to facilitate a transition of the rationale for policy intervention from

the typically limited input-output (linear) approach to a more ‘systemic efficacy’ approach.

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Despite its theoretical attractiveness, however, the SI concept has been criticised for its simplistic

focus on structural elements and its tendency to overlook the dynamics of the respective innovation

systems (Bergek et al., 2008; Edquist, 2005). So far, the SI framework has not been operationalised

sufficiently to enable policy analysts to work with tools that help them develop relevant practical

policy guidelines (Al-Saleh, 2010; Carlsson et al., 2002; Lundvall, 1992). A recent trend is the

emergence of a growing body of work that attempts to go beyond the static description of

innovation systems as fixed constructs and more as dynamic functional systems. Consequently,

several functions lists have recently been proposed in the SI literature (a comprehensive review of

these function lists has been provided by Bergek et al., 2008). For instance, based on the work of

Mahroum et al. (2008), Moreau and Mekkaoui (2009) sought to develop a model for regional

innovation systems, where the main system functions are ‘learning’, ‘recognising value’,

‘appropriating’ and ‘creating new value’. There is also a growing interest among SI scholars in the

effectiveness of places not only in generating new products and services, but also in their ability to

foster links between local and global actors for the benefit of new value creation (Asheim and

Isaksen, 2002; Benneworth and Dassen, 2011; Oinas and Malecki, 2002).

The ‘Efficacy of Innovation System’ is an approach developed in this paper as an attempt to go

beyond the traditional structural-oriented SI analysis, and to examine the underlying processes –

commonly referred to as ‘functions of innovation systems’. ‘Systemic efficacy’ is defined here as the

combined level of efficiency and effectiveness that characterise the operation of a system (Niosi,

2002). In other words, how well do the different components and networks within an innovation

system work together to achieve both individual and collective goals? In effect, the measurement

approach we introduce in this paper helps mitigate the shortcomings of the traditional SI approach,

and complements the growing literature on learning dynamic innovation systems through

highlighting the role of efficacy. This is important because one cannot appropriately assess the weak

links in an innovation system if the value of the different links is not understood properly. For

example, strong university-industry links might be very important in one economy but less so in

another setting. Consequently, a system characterised by a weak university-industry links is

problematic only if and when such links are important for the innovation system to work well. The

variation in the importance of different links to different systems of innovations has very much to do

with the type of knowledge base upon which an innovation system is based (Asheim and Gertler,

2005; Asheim et al., 2007; Plum and Hassink, 2011). Hence, once the value of certain links has been

established, the efficiency and effectiveness of the transactions between the relevant components

of the systems could become possible targets for policy analysis and subsequently measurement.

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The work of Aksentijevid and Ježid (2009) has implicitly touched on some of these aspects by

proposing a measurement of the efficacy of technology and innovations as a combined index of

patents, licenses and stamps only.

4. The AC/DC functions model: towards an operational SI framework

As argued earlier, understanding the functional dynamics of innovation systems is a necessary and a

useful analytical supplement to the traditional SI approach as it provides a ‘process’ focus to the

traditional ‘structural’ focus of SI studies. This is important for policymaking, as Bergek et al. (2008)

have indicated “It is in these processes where policymakers may need to intervene, not necessarily

the set-up of the structural components” (pg. 409).

Given the enormous interest in the system functions approach, this paper offers a contribution to

this growing literature by providing a set of five functions (i.e. key processes of innovation systems) –

that are captured in a framework named the ‘AC/DC model’. Nevertheless, unlike previous

contributions that emphasized the need for all of the functions for an SI to perform well, the AC/DC

model shifts the traditional focus of attention from an over-emphasis on developing and exploiting

new knowledge locally to the prospects of value creation through accessing, anchoring or diffusing

knowledge acquired anywhere.

What is often overlooked is that the capacity of a place (country or region) for innovation usually

depends not only on internal, but also on external sources of knowledge that complement internal

ones (Cassiman and Veugeler, 2002; Mahroum, et al., 2008). This is of particular relevance to

developing countries whose technological progress has mostly been achieved through the

absorption and adaptation of existing technologies and know-how as opposed to invention of

entirely new technologies (World Bank, 2008). This underscores the importance for considering both

the innovation absorptive capacity (AC) and development capacity (DC) of the place under

consideration.

In this regard, Mahroum et al. (2008) have developed the so-called ‘AC/DC’ model of innovation (see

Mahroum et al., 2008), which distinguishes between five functions in the ‘innovation through

adoption’ process through highlighting the concepts of Absorptive Capacity (AC) and Development

Capacity (DC). As shown in Figure 2, three functions are related to AC (accessing, anchoring and

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diffusing knowledge) and two to DC (creating and exploiting knowledge). The key contribution of the

AC/DC model is that it announces a radical departure from the conventional view (Model 1), in

which the functions of knowledge creation and exploitation are regarded as start and finish points of

the innovation process, through arguing that AC is the single most important factor for both

knowledge creation and exploitation (Model 2). Moreover, the extents to which different places

draw on AC or DC to create value vary from one place to another.

Figure 2: AC/DC model for innovation through adoption (Mahroum et al., 2008)

The AC/DC model shifts the focus of policymakers and policy analysts from understanding innovation

as a linear process staring with knowledge creation and ending with knowledge exploitation to a

process where value creation is achieved through learning, adopting and adapting. A similar focus

has been recently made by Castellaci & Natera (2011) where using data from 98 countries show

“that innovative capability and absorptive capacity [of nations] are linked by a set of two-way

dynamic relationships”.

The AC/DC model allows policymakers to benefit from a conceptual framework that takes ‘problem-

solving’ and ‘value-creation’ through learning, adoption and adaptation as its core framework of

analysis and measurement rather the capacity to innovate per se. Subsequently, an innovation

system can be understood as one that delivers the five key problem-solving and value-creation

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functions (access, anchor, diffuse, create and exploit) and innovation policy can be structured

around these five key functions of any innovation system. Below is a brief description of the

abovementioned system functions and a more detailed account will be provided in the next section.

Accessing Knowledge is the ability to connect and link to international networks of knowledge and

innovation. Anchoring Knowledge is the ability to identify and domesticate external knowledge

sources including people and organisations. Diffusing Knowledge is the collective ability of a place to

adapt and assimilate new innovations, practices and technologies and spread them in the economy.

Finally, the two classical functions that have traditionally captured the attention of policymakers,

namely Knowledge Creation which is perceived as the ability to generate and bring new knowledge

to the world, and Knowledge Exploitation which is interpreted here as the ability to put into use and

exploit new knowledge for social and/or commercial purposes.

Measuring Innovation Efficacy

An Innovation Efficacy Index (IEI) has been constructed based on the AC/DC functions model

introduced by Mahroum et al., 2008, which in turn embraces a systemic approach to examining

innovation through adoption. Furthermore, IEI work distinguishes between the capacity of an

economy to innovate and its actual performance. On its own, this is a significant improvement over

conventional composite indicators as it allows policymakers to gain a better understanding of the

gap between an economy’s existing innovation capacity and its ability to make efficient use of that

capacity to innovate and create value. Such an insight provides a new dimension to innovation

policy; one that supplements the notion of systemic failures that represent the broad rationale for

government intervention, cf. conventional rationales of market failures.

5. The selection of the indicators in the IEI

The assessment of capacity and performance is carried out through accessing strengths and

weaknesses along the five system functions (Access, Anchor, Diffuse, Create and Exploit). For each of

these functions, an effort was made to compile a preliminary list of indicators, which were

subsequently categorised to ones that gauge potential capacity and others for assessing the actual

performance of a given economy. To facilitate ease of referencing, as shown in Figure 3, indicators

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relevant to tracking the capacity to innovate are termed ‘input indicators’ and those used to gauge

the actual exploitation of that capacity are named ‘output indicators’.

Figure 3: The Innovation Efficacy Index Construct

The process of refining the selection of the indicators, and their assignment to the five key

dimensions of innovation capacity and performance, has been carried out on the basis of both

authors’ own judgement and extensive literature reviews. The calibration has been decided upon

the authors’ judgement of what is commonly used by other scholars in the field (e.g. Rodriguez and

Soeparwata, 2011). It should be borne in mind, however, that the selection of indicators was made

for the sole purpose of operationalising the IEI model and we encourage other researchers to

improve on them and try other variations in order to arrive at better results. In addition, the authors

ran a correlation test for the various variables for 100 countries (see Appendix 1) and the results

were persistently positive. Nevertheless, we caution that the results generated in this paper are

primarily for demonstrative purposes. Going further, the model will benefit from the use of time-

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series data that allows for a stronger assessment of the relevance of the various links between

components of the innovation system and eventually a better evaluation of the efficacy of the

system as a whole.

The full list of the indicators, along with their sources, is provided in the Appendix 2 and what

follows is an account of the five system functions together with their selected input and output

indicators.

5.1 Accessing Knowledge from Anywhere

This function is interpreted as the ability of an economy to link and connect to international

networks of knowledge and innovation. Strictly speaking, it presents the capability of the various

national actors in an SI to secure benefits through network access or membership of regional and

international networks. Whilst not belonging to the SI scholarly camp per se, the work of Tether and

Taja (2008) has highlighted the need for these issues, the importance of which includes privileged

and rapid access to knowledge and information that could allow national actors to utilise external

resources in order to secure local and/or international competitive advantage (Benneworth and

Dassen, 2011; Inkpen and Tsang, 2005; Sotarauta et al., 2011).

The capability to access knowledge resources at comparatively preferential terms (e.g. cost, quality

or speed) contributes to an economy’s overall international comparative advantages. There is no

doubt that countries, and regions, vary in terms of their capacity to access their needs of knowledge,

expertise and skills for the development of specific products and services upon which local value

creation is dependent. In order to capture these differences, Table 2 lists the various indicators

selected in the IEI framework to gauge the system function of knowledge access. It should be borne

in mind, however, that a precise quantification of territorial stocks of knowledge is not possible

because knowledge flows are dynamic. The complexity of the contemporary geography of

knowledge flows means that their measurement is also far from simple. Hence the attractiveness of

using composite indices, as opposed to relying on a single indicator.

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Table 2: Input and Output Indicators for Accessing Knowledge and Innovation

Access Input Indicators Access Output Indicators

Internet Users Value chain presence

Total Broadband per 100 people Breadth of International markets

Extent of Business Internet Use Presence of Advanced Service Providers

Prevalence of Trade Barriers

Infrastructure

5.2 Anchoring Local Knowledge from Anywhere

This function refers to the ability to identify and domesticate external knowledge sources in the local

economy. In effect, anchoring is manifested in the capacity of an economy to attract potential

sources of knowledge (e.g. international talent, foreign firms and investment) and retain them

locally. Whilst a capacity for accessing international knowledge is crucial for gaining a short-term

benefit, in the long run it is the ability to domesticate external knowledge that may contribute to a

sustainable competitive advantage. The capacity to attract and nurture skills and talent is important

because evidence suggests that highly skilled workers are critical actors for the success of an

innovation system (Cooke et al., 2004; Gilsing and Nooteboom, 2006). However, the anchoring of

international knowledge sources, in general, is not an easy affair (e.g. see Mahroum, 2007 and

2008), and the competition between economies for the anchoring of international knowledge

resources continues to be very fierce. Some of the factors that are considered as being relevant to

this system function include bureaucratic procedures, such as the time needed to start a new

business, as well as the strength of laws providing investor protection (Chung and Alcácer, 2002;

Crone and Watts, 2003). As Table 3 demonstrates, such factors usually translate into a strong

performance in attracting and domesticating outside knowledge resources such as foreign direct

investment, skilled migrant labour and other knowledge clustering arrangements.

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Table 3: Input and Output Indicators for Capacity for Anchoring Knowledge and Innovation

Anchor Input Indicators Anchor Output Indicators

Days for Starting a Business Prevalence of Foreign Direct Investment

Dealing with Licences Presence of Clusters

Political Stability FDI Technology Transfer

Regulatory Quality Royalties Paid

Protecting Investors Inward Skilled Migration

Foreign Ownership Restrictions

5.3 Diffusing Knowledge Locally

This system function captures the collective capabilities available for an economy to adopt, adapt

and assimilate new innovations and know-how in a broad manner. The OECD (1968) was perhaps

amongst the first to popularise the importance of judging innovation performance by considering

diffusion, i.e. measuring the level and rate of increase in the use of new products. Knowledge

diffusion is often perceived as a crucial capacity for innovation performance (Edquist, 1997), not to

mention the fact that it represents a telling indicator of the success of the first two functions, namely

accessing and anchoring. In a recent study, Smith and Glasson (2010) affirmed that the first three

functions are critical to support knowledge creation and innovation development in a particular

place. Milton Park, which is a science and business park in the South East of England, was cited as a

notable European example that enjoys all three functions and this has allowed it to produce ample

economic, social and technological benefits for its wider region.

However, many developing countries sometimes do well in terms of accessing and anchoring

knowledge, skills and expertise from external sources, yet fail to spread these successfully across

their economy. A key factor that explains such a failure is a lack of absorptive capacity – a notion

originally articulated at firm-level by Cohen and Levinthal (1990), which was subsequently promoted

by a large number of academics (including Fabrizio, 2009 and Lane et al., 2006). This is of particular

importance to places with little or no significant R&D capabilities and activities.

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Table 4: Input and Output Indicators for Diffusing Knowledge and Innovation

Diffusion Input Indicators Diffusion Output Indicators

Literacy Rates Firm Level Technology Adoption

Quality of Education System Technology Awareness

Availability of Scientists and Engineers Manufacturer Imports (% of Merchandise

Imports)

Extent of Staff Training Production Process Sophistication

E-participation Index ICT Goods Imports

Local Availability of Specialised Research and

Training Services

ISO Certification

Gross Capital Formation

5.4 Creating New Knowledge

This function refers to the ability to generate new knowledge (in the forms of ideas, discoveries,

designs and inventions) in the world. Most advanced economies invest significantly, in developing

their capacity to generate such new knowledge. Hence, investment tends to be high in R&D and an

emphasis is placed on enforcing intellectual property rights. What is often overlooked though is that

value could be created, and problems could be solved within societies simply by accessing, anchoring

or diffusing the existing knowledge base and not necessarily by creating new knowledge or making

groundbreaking discoveries (Bhide, 2008). Here, it is important to bear in mind the distinction, most

notably made by Joseph Schumpeter (1942), between invention and innovation. Whilst the former

refers to the discovery of new ideas, the latter is often perceived to be about taking these ideas to

the market in order to create value out of them (i.e. the fifth system function of ‘knowledge

exploitation’). Indeed, new knowledge creation does not stem from a vacuum, but usually arises

from the further deepening and expansion of existing knowledge (Simmie et. al, 2008). However,

recognising that – in some instances – value could be created as a result of making new insights, it

was decided to include ‘creating new knowledge’ as a system function in the IEI framework. To that

end, Table 5 illustrates the set of indicators that are considered as being relevant for gauging an

economy’s potential and actual performance when it comes to knowledge creation.

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Table 5: Input and Output Indicators for Creating New Knowledge

Creation Input Indicators Creation Output Indicators

Company Spend on R&D Scientific Publications per Researcher

Intellectual Property Protection, including

anti-counterfeit measures

Patents Filing

Quality of Scientific Research Institutions Trademarks per Firm

Enrolment in Doctoral Programmes Firm/business formation

Researchers in R&D (per million people) All Tertiary Graduates

Tertiary Science Graduates

5.5 Exploiting Knowledge Developed Anywhere

This function is interpreted here as the ability to mobilise and exploit new knowledge for social

and/or commercial purposes. This is a key system function because without it not only can

economies not benefit from any new knowledge, but they would also run the risk of losing talent,

firms as well as investors to other economies that are better equipped to take advantage of their

resources. As shown in Table 6, exploitation capacity is affected by a range of factors including the

availability of venture capital, equity markets and high-quality training establishments. The existence

of such factors could contribute to an increased value added in the economy that will eventually

translate into better living standards.

Table 6: Input and Output Indicators for Exploiting Knowledge and Innovation

Exploitation Input Indicators Exploitation Output Indicators

Venture Capital Availability Goods Exports

Quality of Management Schools Service Exports

Local Equity Market Access Creative Goods & Services

Government Procurement of Advanced

Technology Products

GDP per capita

Entrepreneurship Industry Value Added

Gross Private Capital Flows Services Value Added

As a final remark in this section, it is worth remembering that the traditional models of innovation

used to assume a linear, and causal, link between knowledge creation and exploitation. Such beliefs

are underpinned by the ‘technology push’ doctrine, which assumes that basic research lead to

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applied research that is then transferred into innovation, which in turn lead to greater growth. Such

an approach overlooks the role of knowledge adoption and diffusion by market forces and

knowledge networks in generating innovation. Nor is the reverse linear relationship (i.e. demand

pull) necessarily true because it is not always markets that are the ones to stimulate a search for

knowledge that create value by solving problems. In other words, not only does knowledge creation

itself not necessarily lead to commercially exploitable innovations, but markets do not always

generate sufficient demand for knowledge to develop innovations and create value in a given

economy (Mahroum et al., 2008). The IEI, which is informed by Systems of Innovation thinking,

announces a departure from such traditional ‘linear’ approaches to innovation studies.

6. IEI: Bringing together the various AC/DC components

IEI provides a composite index for each of the five system functions of the AC/DC model. Whilst

acknowledging the limitations of using composites (see OECD and EC JRC, 2008) and, at the same

time, urging caution when interpreting the figures, the IEI provides a useful benchmark for

economies and allows for correlation with the other components of the innovation model and with

economic outputs. Since each of the five system functions is gauged in terms of a range of

indicators, an effort was made to normalise them in order to facilitate comparison. Bearing in mind

the challenges and potential shortcomings associated with various types of treatment (a recent

review of such concerns was provided by Grupp and Schubert, 2010), the normalisation method

used here is known as the ‘min-max technique’. Thus, the IEI normalises indicators to have an

identical range (1 – 7) by subtracting the minimum value and dividing by the range of the indicator

values. One potential downfall of this method is that extreme values (or outliers) could distort the

transformed indicator. Here, the data is normalised so that all indicators are recalibrated to fall

within a range of 1 to 7, with the highest score at 7 and the lowest score at 1.

Normalisation formula for positive series (where high is best, such as GDP per capita):

Normalisation formula for negative series (where low is best, such as time to start a business):

Thus, all variables are converted to a scale of 1-7, with 1 being lowest and 7 being highest.

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Total scores for capacity and performance are calculated as the sum of averages for each indicator

group (i.e. access, anchor, diffusion, creation, exploitation).

Where: is the total score for capacity or performance.

is the average for each indicator group.

Simply put, total capacity and total performance give an aggregate score for the five system

functions. Within each input/output pillar, the scoring is a non-weighted average with a possible

maximum of seven. Total input and total output are scored as a sum of individual input/output

pillars, giving a maximum possible score of 35 each for capacity and performance.

7. Operationalising the IEI Work

This section provides an illustrative example of how the IEI can be used to generate policy analysis.

In particular, a set of resource-rich national economies was chosen to illustrate the discussion.

Countries considered here are Australia, Brazil, Chile, Malaysia, New Zealand, Norway, Sweden and

the United States. The following paragraphs will provide a solid, numerical evaluation of the

theoretical discussion conducted thus far.

To begin with, Table 7 provides a correlation matrix of the Input and Output pillars for the chosen

set of countries. The first column represents the five capacity pillars of Access, Anchor, Diffusion,

Creation and Exploitation. The first row represents the corresponding performance pillars. With the

exception of the capacity and performance Anchor, all correlation coefficients are fairly high. This

provides a simple illustration of the degree of co-movement between the innovation inputs and

outputs, as modelled in the IEI. When judging these results, it is important to remember the old

adage: “Correlation does not imply causation”. This means that while the cells of this table present

empirical evidence on the degree of co-movement between the five system functions, one cannot

put a causal interpretation onto these numbers. Establishing causality would essentially require

regression analysis to be carried out, along with a carefully chosen set of covariates.

19

Table 7: Correlation Coefficients between Capacity and Performance Pillars

Access Anchor Diffusion Creation Exploitation

Access 0.436 0.232 0.936 0.968 0.525

Anchor 0.080 -0.035 0.515 0.570 0.196

Diffusion 0.572 0.413 0.963 0.863 0.616

Creation 0.515 0.338 0.924 0.821 0.590

Exploitation 0.868 0.783 0.740 0.525 0.664

Whilst the reported figures could support the idea that innovation inputs are positively associated

with innovation outputs, caution should be exercised when interpreting these correlations because

of the normalisation, which has been carried out to allocate scores to the various pillars. As

explained in Section 6, the chosen normalisation procedure transforms the actual data to scores

between one and seven. This creates a comparative picture of performance, in the sense that scores

are dependent on the set of countries under consideration. Thus, calculation of correlations on these

normalised scores is not really equivalent to the calculation of correlations on the raw database

itself.

Figure 4 presents a type of policy analysis that can be carried out using the theoretical model

presented in this paper. This graph charts the cross-sectional variation in capacity and performance

along the Access pillar for the set of chosen countries. Upward movement along each axis signifies

higher scores along the corresponding dimension. As shown in Figure 4, the United States ranks

highly on the Access pillar of both Capacity and Performance, which is an expected result. An

unexpected finding is that of Norway, which although ranks quite high on this pillar, it fails to live up

to its potential in terms of realised innovation performance. This hints at institutional obstacles,

which encumber the country from translating innovation inputs into outputs.

20

Figure 4: Cross-sectional Access Capacity and Performance

Figure 5 replicates the analysis presented in Figure 4, but for the Anchor pillar. The dotted line,

which presents a linear trend in the data, shows a weak (almost non-existent) relationship between

capacity and performance. While this could be due to holes in the raw data and the selection of

countries, it may also mean (if taken at face value) that anchoring inputs are more challenging in

achieving desired outcomes such as attracting further complimentary resources to a region. This is

particularly so in the case of countries that are geographically peripheral such as Australia, Chile,

New Zealand and Norway.

Figure 5: Cross-sectional Anchor Capacity and Performance

21

Figures 6 – 8 present the cross-sectional analysis for Diffusion, Creation and Exploitation

respectively. Although much of the previous discussion applies to these figures as well, another

point that should be of particular interest to policymakers is that such IEI-based visualisations

provide a means for policymakers to identify ‘under-performing’ or ‘over-performing’ countries. In

other words, under-performing countries are those that score low on the performance scale while

scoring very high on the innovation capacity scale. Despite a reliance on overarching aggregates at

the national level, consistent under-performance usually indicates the presence of institutional

hindrances. Analogously, ‘over-performance’ refers to countries which score (relatively) higher on

the performance scale as compared to their capacity scale. The policy implication here is that

innovation performance of these countries can be further enhanced by incremental increases in

inputs. Considering the set of countries in hand, Australia and New Zealand seem to be under-

performers; while there do not seem to be any clear over-performers.

Figure 6: Cross-sectional Diffusion Capacity and Performance

22

Figure 7: Cross-sectional Creation Capacity and Performance

Figure 8: Cross-sectional Exploitation Capacity and Performance

Finally, we present an aggregation of this discussion in the form of Figure 9. This figure presents

cumulative performance and capability scores for the given set of countries. These cumulative scores

are calculated by taking a simple summation over the individual pillars. More importantly, the

dotted (45 degree) line represents points where the horizontal and vertical axis values are the same.

This means that if a country resides on this line, its cumulative performance score is equal to its

23

cumulative capacity score. Being below the line means that capacity outweighs performance

(‘Under-performers’) and being above the line means that performance outweighs capacity (‘Over-

performers’). Based on the data, the two discernible over-performers are the United States and

Brazil. The former usually ranks fairly high on most innovation indicators and generally considered to

be one of the most innovative countries in the world. The latter is actually a surprising result:

although Brazil ranks fairly low in terms of its absolute levels of innovation input and output, it is

actually fairly efficacious in turning those inputs into outputs. As such, it can provide a role-model

for nascent knowledge economies such as the United Arab Emirates – (i.e. economies with low

extant levels of innovation capacity and outputs) – to improve the efficacy of their innovation.

Figure 9: Aggregate Innovation Scores

8. Concluding Remarks

The AC/DC framework and the resultant IEI measurement tool introduce a new dimension to

measuring the performance of economies in terms of generating innovations. While traditional

measurement tools and indices have sought to capture inputs and outputs, the IEI uses inputs and

outputs indicators as metrics to (i) measure the efficacy of socio-economic systems in generating

innovations; and (ii) identify strengths and weaknesses in terms of specific functions rather than

outputs. This is an important addition because an innovation policy guided by input-output

24

measurements alone runs the risk of confusing symptoms with problems (outputs with inefficiency

problems) and might take competitor level of inputs as benchmarks for optimal levels of

investments needed.

Additionally, the IEI provides a functions-based operational ‘Systems of Innovation’ framework with

a strong emphasis upon the process of innovation through the adoption. Rather than the traditional

focus on the capacities to create and exploit new knowledge locally, it has been argued that new

value is often created by accessing, anchoring or diffusing existing knowledge. In this regard, we

believe that adopting the AC/DC conceptual framework aided by the IEI measurement tool will allow

policy analysts extract practical policy guidelines from SI studies.

The IEI framework also allows for measurements to be developed to fit with the type of knowledge

base that supports a specific territorial (or sector-specific) innovation system (as suggested by

Asheim and Gertler, 2005) and the economic and industrial structures of economies. Consequently,

the IEI tool allows for benchmarking innovation systems for the purpose of cross-context learning

rather than a cross-country generic search for best practice, high performers and low performers.

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Appendix 1: Testing the Selection of the IEI indicators

An effort was made to assess the associations between the various indicators selected in the

IEI. This test has been conducted on the data available for more than 100 countries (listed

below).

Albania Denmark Kazakhstan Norway Timor Leste

Algeria Ecuador Kenya Oman Tunisia

Argentina Egypt Latvia Pakistan Turkey

Armenia El Salvador Lesotho Panama Uganda

Australia Estonia Libya Paraguay Ukraine

Austria Ethiopia Lithuania Peru United Arab Emirates

Azerbaijan Finland Luxembourg Philippines United Kingdom

Bahrain France Madagascar Poland United States

Bangladesh Georgia Malawi Portugal Uruguay

Barbados Germany Malaysia Qatar Venezuela

Belgium Ghana Mali Romania Vietnam

Benin Greece Malta Russia Zambia

Bolivia Guatemala Mauritania Saudi Arabia Zimbabwe

Botswana Guyana Mauritius Senegal Bosnia and

Herzegovina

Brazil Honduras Mexico Serbia Brunei Darussalam

Bulgaria Hungary Mongolia Singapore Burkina Faso

Burundi Iceland Montenegro Slovenia Costa Rica

Cambodia India Morocco Spain Czech Republic

Canada Indonesia Mozambique Suriname Dominican Republic

Chad Ireland Namibia Sweden Gambia

Chile Israel Nepal Switzerland Hong Kong SAR

China Italy Netherlands Syria Slovak Republic

Colombia Jamaica New Zealand Tajikistan South Africa

Croatia Japan Nicaragua Tanzania Sri Lanka

Cyprus Jordan Nigeria Thailand

As shown in the following scatter diagrams, the results for the five system functions are

persistently positive. This provides empirical evidence that supports the relevance of the

indicators selected in IEI in order to gauge an economy’s capacity for innovation and its

actual exploitation of that capacity.

33

More specifically, the ‘green dots’ represent individual countries and the ‘dotted line’

represents a linear upwards trend in the dataset for a particular system function. This

positive correlation suggests that those countries which score highly in their capacity to

innovate along the five dimensions, manage to turn that into noticeable performance. The

positive trend is relatively weaker in the case of the Anchor function given the challenges,

and time usually taken, to translate anchoring inputs into desired outcomes such as

attracting and domesticating foreign investment and knowledge resources.

34

35

36

Appendix 2: List of the IEI Indicators

The IEI uses a selection of key indicators to track both an economy’s capacity for innovation

and its actual exploitation of that capacity. The capacity to innovate is tracked through

‘capacity’ variables. The exploitation of that capacity is tracked through ‘performance’

variables.

A high or low score on a given indicator is not good or bad, per se. Rather, it can indicate

consistency with policy objectives and can be used to track achievement of policy targets.

Accordingly, a high score on an area of policy priority is a positive indicator, while a low

score in a non-priority area is a neutral indicator.

Table 1: Input Indicators

INPUT Variable Definition Source

Access 1 internet users Broadband users per 100 inhabitants

International Telecommunications Union

2 Total broadband per 100 people

Broadband available per 100 inhabitants

International Telecommunications Union

3 Extent of internet business use

Extent of business internet use World Economic Forum, Executive Opinion Survey

4 Prevalence of trade barriers

Trends in Average MFN Applied Tariff Rates in Developing and Industrial Countries

World Bank

5 Infrastructure Quality of overall infrastructure World Bank

Anchor 1 days for starting a business

Time (in days) to start a business World Bank, Ease of Doing Business

2 dealing with licenses

Number of procedures to start a business

World Bank, Ease of Doing Business

3 political stability Political Stability and Absence of Violence/Terrorism

World Bank Governance Indicators

4 regulatory quality The ability of government to formulate and implement policies that permit and promote private sector development

World Bank Governance Indicators

5 protecting investors

Strength of investor protection World Bank, Ease of Doing Business

6 Foreign Ownership Restrictions

Prevalence of foreign ownership World Economic Forum, Executive Opinion Survey

Diffusion 1 literacy rate Adult literacy rates UNESCO

2 Quality of Education System

Quality of education system World Economic Forum, Executive Opinion Survey

3 Availability of scientists and engineers

Availability of scientists and engineers

World Economic Forum, Executive Opinion Survey

4 Extent of staff training

Extent of staff training World Economic Forum, Executive Opinion Survey

37

5 E-participation index

quality, relevance, usefulness, and willingness of government websites for providing online information and participatory tools and services


6 Local availability of specialised research and training services

Local availability of specialized research and training services


7 ISO Certification Extent of ISO registration International Organisation for Standards

8 Gross Capital Formation

Gross capital formation (%GDP) UNCTAD

Creation 1 R&D spending Company spend on R&D World Economic Forum, Executive Opinion Survey

2 IPR protection Intellection property protection, including anti-counterfeit measures


3 Quality of scientific research institutions

Quality of scientific research institutions


4 Enrolment in Doctoral programme

Enrolment in PhD programmes UNESCO

5 Researchers in R&D (per million people)

Researchers in R&D per million population

World Bank

Exploitation 1 Venture Capital Availability

Venture capital availability World Economic Forum, Executive Opinion Survey

2 Quality of management schools

Quality of Management Schools World Economic Forum, Executive Opinion Survey

3 Local Equity Market Access

Ease of raising funds through equity market

World Bank

4 Government procurement of advanced technology products

Government procurement of advanced technology products


5 Entrepreneurship Total Entrepreneurial Activity. Number of Adults per 100 involved in a nascent firm.

Global Entrepreneurship Monitor

6 PISA scores General science PISA score OECD

38

Table 2: Output Indicators

OUTPUT Variable Definition Source

Access 1 Value Chain presence

Value Chain Breadth World Economic Forum, Executive Opinion Survey

2 Breadth of international markets

Foreign market size index: Value of exports of goods and services

3 Advanced Services Producers presence

Presence of advanced service provider offices in main city

Loughborough University

Anchor 1 Foreign Direct Investment

FDI net inflows World Bank Development Indicators

2 Presence of clusters

State of cluster development World Economic Forum, Executive Opinion Survey

3 FDI & Tech Transfer

Extent of FDI bringing new technology into country


4 Royalties paid (normalisation)

Royalty and license fees, payments

World Bank Development Indicators

5 Inward skilled migration

Stock of inward migration (2000-2005) with advanced education

OECD

Diffusion 1 Firm level Technology Adoption

Firm-level technology absorption World Economic Forum, Executive Opinion Survey

2 Technology Awareness (buyer sophistication)

Buyer sophistication World Economic Forum, Executive Opinion Survey

3 Manufacturers imports (% of merchandise imports)

4 Production process sophistication

Production process sophistication


5 ICT goods imports ICT goods imports (% total goods imports)


Creation 1 Scientific publications per researcher

Scientific publications World Bank Development Indicators

2 Patents filing Number of Patents Granted as Distributed by Year of Patent Grant Breakout by Country of Origin

United States Patent and Trademark Office

3 Trademarks per firm

Trademark applications, direct resident


4 Firm/business formation

Business entry rate (new registrations as % of total)


5 All Tertiary Graduates

Graduates in all tertiary education

UNESCO

6 Tertiary Science Graduates

Graduates in science tertiary education

UNESCO

Exploitation 1 Goods exports Value and shares of merchandise exports and imports


2 Service exports Value and shares of exports and imports of services


39

3 Creative goods & services

Creative industries international trade as percentages of total World

4 GDP per capita GDP per head ($ at PPP) Economist Intelligence Unit

5 Industry Value Added

Industry, value added (% of GDP) World Bank Development Indicators

6 Services Value Added

Services, etc., value added (% of GDP)

UNDP Human Development report, plus others

7 Gini Index Income inequality World Bank Development Indicators

Documents

Measuring Innovation Efficacy: An Operational Framework ... · the underlying conceptual thinking behind most innovation measurement tools, which is the linear model of innovation