2 | Social Capabilities for Catching-Up through Sustainability Innovations
2.2 General Innovation Capabilities
It is more and more acknowledged that the absorption of technologies and the development of abilities to further advance these technologies are closely interwoven. Technology absorption can be seen as an innovative activity since it is equally based on learning and breaking up with established routines as the development and utilization of indigenous technologies. Hence, the necessary social capabilities for successful technology absorption do largely overlap with those for a functioning national innovation system (NIS).
The heuristic of the innovation system, which more recent innovation re-search calls on to explain innovative activity (Lundvall, 1992; Nelson, 1993;
Lundvall et al., 2002; Carlsson et al., 2002; Edquist, 2005), has gained relevance for the analysis of catching-up processes, and the building-up of innovation ca-pabilities has become a centrepiece of national catching-up strategies. Within this theoretical framework, the generation and diffusion of new technological solutions depend on social interactions of various agents within the innova-tion system. Innovainnova-tion system scholars stress the interactive and non-linear character of the innovation process, which is influenced by many complexly interlinked economic, social and political institutions. As numerous influential factors and their mutual interdependences can only be quantified with diffi-culty, measuring national innovation capabilities, and hence assessing the ab-sorptive capacity, remains a problematic issue.
The recognition of the important role of innovation for economic devel-opment and catching-up has led to an increased effort to extend innovation performance measurement to less developed countries. However, the part of innovative activity that predominantly drives technological change in devel-oping countries, i.e. knowledge diffusion through imitation and adaptation efforts or through the adoption of capital goods as new machinery and equip-ment, is hardly captured by traditional innovation indicators, such as scientific publications, R&D expenditures, patents or patent applications.
In order to obtain a broader picture, composite synthetic indicators that
ag-gregate different types of indicators into simpler constructs, have been used more frequently over the last years to measure and compare the technological capabilities or the innovation performances of nations (Archibugi and Coco, 2005). Archibugi et al. (2009) give an overview on the state of the art and dis-cuss the value of these exercises. They conclude that composite indicators are particularly useful to single out the differences within relatively homogeneous groups of countries.
By constructing a composite synthetic indicator that reflects the quality of the main innovation system activities, a method to compare the framework conditions of national economies for the generation, diffusion and utilization of new technological knowledge shall be developed and applied to a group of newly industrializing countries in order to detect specific strengths and weak-nesses of their NISs. This composite indicator is supposed to not simply mea-sure the actual innovation performance, but to assess in a broader sense a sys-tem’s overall capability to dynamically self-transform. In concordance with David and Foray (1995), innovation capability is understood as being not only the ability to discover new technological principles, but rather the ability to exploit systematically the effects produced by new combinations and use of pieces in the existing stock of knowledge (Freeman and Soete, 2009).
In contrast to most synthetic indicator approaches in the field, a large num-ber of exclusively subjective single indicators are used in order to measure the functioning of the innovation system. Data originate from expert opinion sur-veys of the World Economic Forum (WEF, 2006, 2008) and the Institute of Man-agement Development (IMD, 2006) published in periodical competitiveness re-ports. For reasons of generalizability of the empirically determined weights of the indicator composition, data for 55 high and middle income countries are included.
Even though the reliability of subjective data is at times questioned, the relevant tacit aspects concerning the well-functioning of an innovation system may be better reflected in opinions than in supposedly objective counts and the results more robust against cross-country differences in the system structures compared to hard data evaluations that often enough are subject to measure-ment biases. Moreover, survey respondents may describe the current situation of the innovation system by abstracting from noisy, short-term fluctuations that usually exist in hard data.
The concept of system functions provides a useful tool to organize the com-parison of different NISs since it allows uncoupling the activities in the
sys-tem from the syssys-tem’s structure, i.e. the contributions towards technological change from the contributing system components. In comparative studies of heterogeneously structured systems this is an important analytical advantage (Liu and White, 2001).
Various attempts have been made to list the most important activities in innovation systems. Johnson (2001) compares different approaches, thereby identifying basic functions that are mentioned by most authors in the field.
The taxonomy has been further systematized by Hekkert et al. (2007). Follow-ing this work, data selection is structured based on the followFollow-ing functions:
KNOWLEDGE DEVELOPMENT,KNOWLEDGE DIFFUSION,RESOURCE MOBILIZA
-TION, ENTREPRENEURIAL EXPERIMENTATION, MARKET FORMATION, COUN
-TERACTING RESISTANCE, andGUIDANCE OF SEARCH.
Inspired by the triple helix concept (Leydesdorff and Meyer, 2006), we dis-tinguish three generic subsystems of the national system of innovation in or-der to structure the indicator selection, without consior-dering specific actors: the academic, the industrial, and the governmental subsystem. Along the two di-mensions of ‘subsystems’ and ‘functions’ of the NIS a total of 46 indicators are selected for further analysis (see Table 2.1).
Given the large number of indicators, the complexity of the data set has to be reduced in order to describe and compare the strengths and weaknesses of the different NISs. Usually, composite indicators are built by grouping related indicators thematically and weighing them together. However, this approach has been heavily criticized for being arbitrary and thus opening up a large space for manipulation (Grupp and Mogee, 2004).
An alternative approach, pioneered by Adelman and Morris (1965) and al-ready applied to the measurement of innovation capabilities by Fagerberg and Shrolec (2008), is the so-called factor analysis, where indicator weighting is em-pirically derived from the data. The fact that indicators referring to the same dimension tend to be strongly correlated is used here to extract a small number of factors that jointly explain a large part of the variation in the data set.
Following this methodology, principal component analysis is employed to examine the underlying structure for the main contributions to the innova-tion system. Applying the Kaiser criterion and standardized Varimax rotainnova-tion, five orthogonal factors with an eigenvalue bigger than one are retained that together explain more than 80% of the total variance.
Based on the factor loadings (see Figure 2.1), the following interpretations of the retained factors are given:
Table2.1:Selectedinnovationsystemindicators KNOWLEDGE DEVELOPMENT
KNOWLEDGE DIFFUSION
RESOURCE MOBILIZATION
ENTREPRENEURIAL EXPERIMENTATION
MARKETFORMATIONCOUNTERACTING RESISTANCE
GUIDANCEOF SEARCH x1Basicresearchdoes enhancelong-term economicdevelopment x2Qualityscientificresearch institutions x3Availabilityofscientists andengineers
x9Localavailabilityof specialisedresearchand trainingservices x10University-industry researchcolaboration x19Qualityoftheeducational system x20Qualifiedengineersare availableinyourlabour market
x28Qualityofmanagement schools Academicsubsystem x4Capacityforinnovations x5Availabilityoflatest technologies x6Productionprocess sophistication x7Natureofcompetitive advantage
x11Degreeofcustomer orientation x12Firm-leveltechnology absorption x13FDIandtechnology transfer x14Ethicalbehavioroffirms x15Stateofcluster development x16Technologicalcooperation betweencompaniesis developed x21Companyspendingon R&D x22Fundingfortechnological developmentisreadily available x23Financingthroughlocal equitymarket x24Skilledlabourisreadily available x25Foreignhigh-skilled peopleareattractedto yourcountry’sbusiness environment x29Venturecapitalavailability x30Hiringandfiringpractices x31Easeofaccesstoloans x32Entrepreneurshipof managersiswidespreadin business
x36Buyersophisticationx39Extentofmarket dominance x40Intensityoflocal competition Industrialsubsystem x8Scientificresearchis supportedbylegislation
x17Intellectualproperty protection x18Developmentand applicationoftechnology aresupportedbythelegal environment x26Businessimpactofrules onFDI x27Qualityofoverall infrastructure
x33Creationoffirmsis supportedbylegislation x34Technologicalregulation supportsbusiness developmentand innovation x35Labourregulations (hiring/firingpractices, minimumwages,etc.)do nothinderbusiness activities x37Governmentprocurement ofadvancedproducts x38Presenceofdemanding regulatorystandards
x41Protectionismdoesnot impairtheconductofyour business x42Effectivenessof anti-monopolypolicy x43Favouritismindecisionsof governmentalofficials
x44Transperencyof governmentpolicymaking x45Governmentdecisionsare effectivelyimplemented x46Policydirectionofthe governmentisconsistent Governmentalsubsystem
Figure2.1:Factorloadingsforretainedinnovativenessfactors
F1(Learning). The first factor is labelled ‘learning’ as it correlates highly with variables describing the ability to generate and diffuse knowledge (x21, x2,x9,x17,x12,x36,x15) and with the general technological standing (x6, x7,x5,x27) which, given the accumulative nature of knowledge, supports all kinds of learning. It is not surprising that this factor contributes most to the functioning of the NIS, since interactive learning is at the heart of the any innovation process.
F2(Governance). The second factor is labelled ‘governance’, as it shows high correlation with variables related to governmental policy (x45,x41,x46, x44,x43) and legislative framework conditions (x34,x33,x17). Some vari-ables, related to the technological sophistication (x5],x27), to mutual trust (x14,x38,x44) and to the securing of competition (x42,x43), load highly on both the first and the second factor, which underlines their fundamen-tal and cross-cutting importance for the NIS. In addition to the already mentioned learning effect, an advanced technological standing also en-ables better communication (and hence knowledge diffusion) and pro-vides means for better governance. The importance of mutual confidence or honesty and trust for economic development has been emphasized by several scholars.
F3(Entrepreneurship). The third factor ‘entrepreneurship’ is primarily com-posed of variables which describe framework conditions that allow a flexible implementation and the establishment of new business ideas, as variables related to the labour market (x30, x35, x25), to the en-trepreneurial spirit (x32) and to the promotion of new markets (x33,x37).
Basically, this factor measures conditions that ensure creative competi-tion and the challenge of established actors by new market entries.
F4(Openness). The fourth factor is labelled ‘openness’ as it comprises vari-ables that reflect the inward openness of the economic system (x13,x26, x25) and the degree of competition therewith connected (x40,x41). The access to foreign knowledge, the adaptation or imitation of technolo-gies developed elsewhere is seen as one of the central mechanisms of catching-up processes. According to Lall (1992) technological capabil-ity does not only depend on domestic technological efforts, but also on foreign technology acquired through imports of machinery and foreign direct investment.
Figure 2.2: Innovation System Index ranking
F5(Skilled labour). The fifth factor is labelled ‘skilled labour’ as it correlates highly with indicators reflecting the availability of qualified human capi-tal (x20,x24,x3), the most important resource for the innovation process.
As expressed by Metcalfe and Ramlogan (2008), only people can know and only activity in brain cells can lead to change in knowledge and for knowledge to lead to social and economic action it must to a considerable degree be shared across individuals. Consequently, the way knowledge is distributed and the extent to which knowledge is diffused among the individuals influences its growth.
Using only the first principal component, the overall innovation capabilities can be reduced to one single number. The main advantage of this composite indicator is that it synthesizes all the different aspects of the innovation system, but of course only at the expense of information reduction. However, it still explains more than 63% of the overall variance in the data set. Additionally, the five innovation system factors can be used to describe the composition of the main indicator value, which is labelled Innovation System Index (ISI) since it is supposed to reflect the well-functioning of the whole NIS. The ISI is a linear combination of the five retained factors with the following empirically determined weights: 40.7% "learning", 23.3% "governance", 14.6% "openness", 12.9% "entrepreneurship" and 8.5% "skilled labour".
In Figure 2.2 the 16 newly industrializing countries are ranked based on their ISI value. Singapore shows by far the best performing overall NIS. The leadership in terms of innovation capabilities of the Asian Tiger states Korea, Taiwan, Malaysia and Singapore compared to the other countries is striking, as are the respective shortcomings of the Latin American countries Venezuela and Argentina.
In order to assess the quality of the developed country ranking, the com-posite indicator values are cross-examined using different types of data. The ISI behaves well, showing highly significant positive correlations when com-pared to other innovation performance and technological capability indices, as the ArCo Index (Archibugi and Coco, 2004), the UNCTAD Innovation capa-bility index (UNICI) from the World Investment Report (UNCTAD, 2005), the Knowledge Index (KI) and the Knowledge Economy Index (KEI) of the World Bank (The World Bank, 2008), the Competitive Industrial Performance Index (CIP) from the Industrial Development Report (UNIDO, 2009), and the Global Summary Innovation Index (GSII) of the European Commission (Hollanders and Arundel, 2006).
Not surprisingly, the highest correlation is found with the WEF Innova-tion Factor (IF) from the World Competitiveness Report (WEF, 2008) which is based on the same expert opinion results as part of the ISI, though employing a much smaller number of indicators and somehow arbitrarily defined indicator weights.
Based on the five retained factors that describe each innovation system, cluster analysis is now performed to identify groups among the 16 countries of both similar performance levels and similar patterns concerning the strengths and weaknesses of the NISs. While the ISI could give identical ratings for two countries that have diametrically different performance profiles, cluster assign-ment includes the information on the composition of the innovation capabili-ties.
Two different cluster methods are applied: absolute performance clusters (APC) are based on the Euclidean distance measure, while relative perfor-mance clusters (RPC) are based on the Pearson correlation similarity measure.
While absolute performance clusters take both the performance levels and pat-terns into consideration, relative performance clusters compare only the pro-files of strengths and weaknesses, and therefore may combine countries with very different absolute innovation capabilities.
Country groupings based on absolute performance follow closely the ISI
Table 2.2: Correlations of the ISI and other innovation indices
Variables ISI IF KEI KI GSII CIP ArCo UNICI
Innovation System Index (ISI)
1.000 N=55
WEF Innovation Factor 2009 (IF)
0.938 1.000 (0.000)
N=55 N=55
WB Knowledge Economy Index 2008 (KEI)
0.721 0.707 1.000 (0.000) (0.000)
N=55 N=55 N=55
WB Knowledge Index 2008 (KI)
0.667 0.684 0.980 1.000
(0.000) (0.000) (0.000)
N=55 N=55 N=55 N=55
Global Summary Innovation Index 2005 (GSII)
0.875 0.935 0.738 0.756 1.000
(0.000) (0.000) (0.000) (0.000)
N=40 N=40 N=40 N=40 N=40
Competitive Industrial Performance Index 2005 (CIP)
0.671 0.738 0.557 0.520 0.649 1.000
(0.000) (0.000) (0.000) (0.000) (0.000)
N=52 N=52 N=52 N=52 N=39 N=52
ArCo Index of Technological Capabilities 2004
0.769 0.821 0.884 0.905 0.932 0.559 1.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
N=55 N=55 N=55 N=55 N=40 N=52 N=55
UNCTAD Innovation Capability Index (UNICI)
0.617 0.655 0.921 0.961 0.759 0.462 0.900 1.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000)
N=53 N=53 N=53 N=53 N=40 N=51 N=53 N=53
ranking, yet separate the medium performing countries in groups of different innovation system characteristics. The average compositions of the ISI value by innovation capabilities can be observed in Figure 2.3(a), where absolute per-formance clusters are ranked based on the ISI and the factor contributions are depicted.
To further analyse the patterns of strengths and weaknesses among the NISs, relative performance clusters are compared. In order to illustrate the relative strength of each innovation system factor, the deviation from the av-erage is divided by the standard deviation of the country’s factor values. By doing so, we obtain a profile of strengths and weaknesses for each country.
Figure 2.3(b) shows the average profiles for the relative performance clusters.
The results of both clustering options are very similar (see Table 2.3): The only difference between absolute and relative performance clusters is the mem-bership change of the countries in group 4: China, Indonesia, and Thailand, which in absolute terms match with South Korea and Taiwan, are more similar to Malaysia and Singapore in relative terms. In order to allow for comparison of absolute and relative innovation capabilities, in the following the thereby defined six country groups are referred to.
Table 2.3: Country group characteristics
APC RPC ISI F1 F2 F3 F4 F5
AR, VE (1) (1)
Mean −1.96 −1.11 −0.66 −0.92 −2.07 0.19
F-Value 0.11 0.01 0.02 0.23 0.84 0.13
t-value −1.55 −0.90 −0.28 −0.86 −2.11 0.06
BR, CL, IN,
MX, PH, TR (2) (2)
Mean −0.70 −0.91 −0.23 −0.63 0.58 0.70
F-Value 0.25 0.35 0.68 0.14 0.36 0.24
t-value −0.29 −0.65 0.20 −0.59 0.34 0.55
KR, TW
(3+4) (3)
Mean 0.65 1.23 −1.20 0.50 0.02 0.49
F-Value 0.00 0.12 0.02 0.26 0.42 0.01
t-value 1.06 1.96 −0.88 0.53 −0.17 0.35
CN, ID, TH
(4+5)
Mean −0.58 −0.40 −1.20 0.85 0.64 −0.68
F-Value 0.05 0.06 0.64 0.62 0.53 0.01
t-value −0.17 −0.03 −0.89 0.86 0.40 −0.78
MY, SG (5)
Mean 1.22 0.12 0.90 1.45 0.81 0.74
F-Value 0.57 0.02 0.98 0.22 0.13 0.01
t-value 1.63 0.61 1.45 1.46 0.56 0.60
ZA (6) (6) Mean −0.41 0.16 0.38 −1.38 0.42 −2.96
t-value 0.00 0.66 0.88 −1.32 0.20 −3.00
(a) Innovation capabilities of absolute performance clusters
(b) Strengths and weaknesses of relative performance clusters
Figure 2.3: Comparison of cluster performances
Figure 2.4: Geographical distribution of clusters
Both cluster processes show a clear divide between the old and new Asian Tiger states (except for Philippines) on the one side (groups 3, 4 and 5) and the other newly industrializing countries on the other (groups 1 and 2). Except for the biggest group 2, which contains countries from three different continents, cluster membership seems to be closely associated with geographical proxim-ity (see Figure 2.4). South Africa, as the only African country, is a clear outlier and does not cluster with any of the other countries. In Table 3 we give an overview on the innovation system characteristics of each country group.
To sum up, by combining the analyses of absolute and relative performance clusters, the NISs can be characterized as follows:
Venezuela and Argentina. Among the countries analysed they show the low-est overall innovation capabilities, suffering particularly from an isola-tion of their innovaisola-tion systems, but also revealing lacking capabilities in all the other dimensions, except for a relatively high availability of skilled labour.
Brazil, Chile, India, Mexico, Philippines, and Turkey. Their overall innova-tion capabilities are weak, especially in terms of learning capabilities and the conditions for entrepreneurial experimentation. However, these countries possess an absolute strength in terms of skilled labour, which is comparable to the best-performing group 5, and have relatively open innovation systems.
South Korea and Taiwan. An outstanding learning capability but relatively weak governance characterize the overall well-performing NISs.
China, Indonesia, and Thailand. These countries show only a mediocre over-all innovation capability, which is primarily due to an absolute weakness in governance and a relatively weak supply of skilled labour. In contrast, they show relatively good conditions for entrepreneurship.
Malaysia and Singapore. Their leadership in terms of overall innovation ca-pability is based on relatively well-balanced innovation system function performances without any evident deficiencies. Furthermore, they rely on an absolute strength in terms of governance and conditions for en-trepreneurial activity.
South Africa. Its innovation system is characterized by a dramatic lack of skilled labour and an absolute weakness concerning entrepreneurship.
Relatively good governance and learning capabilities nonetheless ac-count for a medium rank as to the overall innovation capability.