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Ameasureoftechnologicalcapabilitiesfordevelopingcountries

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Technological Forecasting & Social Change 92 (2015) 210–223

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Technological Forecasting & Social Change

Ameasure of technological capabilities for developing countries☆

Nabaz T. Khayyat⁎, Jeong-Dong LeeTechnology Management, Economics, and Policy Program, College of Engineering, Seoul National University, San 56-1, Sillim-Dong, Kwanak-Gu, 151-742 Seoul, South Korea

a r t i c l e i n f o

☆ The authors are grateful to anonymous refereesjournal for very useful comments and suggestions on amanuscript.⁎ Corresponding author. Tel.: +82 2 880 9298; fax: +

E-mail addresses: nabaz@snu.ac.kr (N.T. Khayyat)Lee).

http://dx.doi.org/10.1016/j.techfore.2014.09.0030040-1625/© 2014 Elsevier Inc. All rights reserved.

a b s t r a c t

Article history:Received 7 December 2013Received in revised form 6 August 2014Accepted 10 September 2014Available online 26 September 2014

The study was conducted to develop an index as a new measurement tool analyzing theinnovativeness of developing countries. The role of science and technology in enhancing therate of innovation is also investigated. The index is estimated for 61 countries observedduring 2003–2008. The countries are classified into three groups based on their innovationlevel. The highest rate of innovation was noticed in China, followed by Estonia and Malaysia.The lowest innovation rate was reported in Iran, Bangladesh, Tadzhikistan, and Cambodia. Itis recommended that governments (1) to allocate significant share of their budgets to thefactors that enhance technological capability such as the science education, gross educationenrollment rate and internet connectivity, (2) to promote policies of national awards forscientists and researchers who make sound breakthroughs in science and technology, (3) todevelop international relations in the social, economic, cultural, and scientific spheres, (4) tomodify school curriculum and syllabus, so that higher emphasis is given to the creativity andspontaneity of the children, (5) to relax portion of corporate taxes for developing aninnovative way of product and production processes, which are environmentally friendlyand economically viable. Finally, (6) the special focus must be given to the encouragement oflocal organizations to conduct the specialized training programs to promote innovationactivities.

© 2014 Elsevier Inc. All rights reserved.

JEL classification:C19C49I32J24O30O32

Keywords:Innovation indexTechnological capabilitiesInnovation diffusionHuman skillsSocio-economic indicators

1. Introduction

The definition of innovation has evolved over time. InDrucker (1985) innovation is defined as the specific tool ofentrepreneurs and the means for exploiting the change as anopportunity for a different business or services. Damanpour(1991) defines innovation as any practice that is new toorganizations, including equipment, products, services, pro-cesses, policies, and projects. More recently, Afuah (2003)

and the Editor of then earlier version of the

82 2 873 7229., leejd@snu.ac.kr (J.-D.

proposed that innovation is the use of new technical andadministrative knowledge to offer a new product or service tocustomers. It is a process of coming up with new ideas leadingto higher convenience for human existence. In other words,innovation is a gradual process of converting the opportunityinto new ideas which will be further employed for develop-ment of new practices leading to technological advancement(Tidd, 2001).

The inter-relationship between science and technology andinnovation is significant. They both positively influence eachother. The existing literature suggested that the rate ofinnovation and contribution from science and technology hasnot been satisfactory in several developing countries in Asia,Middle East, South America, and Africa (Almeida andFernandes, 2008; Archibugi and Coco, 2004; Fagerberg andVerspagen, 2007). Though some types of methodologies wereused for measuring the extent of innovation in few studies,they lack the clarity in identifying the extent of innovation in

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developing countries and their relative status with that ofdeveloped countries. Moreover, the role of developingscience and technology in enhancing the rate of innovationwere not focused specifically in those studies. For example,the Technology Index developed by the World EconomicForum (WEF, 2011), and the Technology AchievementIndex (TAI) developed by UN Development Program(UNDP, 2001) concentrate much on the extent of advance-ment in technology, rather than creativity and innovation.Similarly, the UN industrial development scorecard devel-oped by UNIDO (2002) emphasizes more on the rate ofindustrial growth which is not fully linked with the rate ofinnovation.

All these methods did not take science and technologydevelopment as a major component of measuring thetechnology indices. Few studies were concentrated onmeasuring science and technology development and growthrate such as Science and Technology Capacity Index by RandCorporation (Wagner et al., 2001). However, its interrelationwith the rate of innovation is yet lacking. Though someefforts were made by researchers to find better measure-ment tools relative to that of the above said methods, theytoo could not integrate the various factors affecting scienceand technology and innovation in an integrated manner.Hence, there is a necessity for identification and standard-ization of new means of estimating the extent of contribu-tion made by science and technology for the innovation indeveloping countries, and accordingly the policy initiativescan be taken.

As well described by Archibugi et al. (2009), there aremainly three benefits to make technological capabilities' basedindex measure: First, theoretical analysis which enables theresearchers to test different innovation theories and theirrelation as a drive engine to the economic growth, second,technological capabilities' based index as a source of informa-tion will enable policy makers to place their countries in aposition where strength andweaknesses can be identified, andaccordingly appropriate innovation policies may be formulatedand finally, such indices may act as inputs for firms' andindustries' strategies to enable managers to understand theextent of the technological advance to better develop theirinnovation activities.

There is a need for increasing the extent of innovationthrough better focus on science and technology and researchto promote and strengthen development in developingcountries. Developing countries should try to match theirrate of development with that of the newly developedcountries such as South Korea. For achieving the samedevelopment, there is a need for identifying the presentrates of innovation and extent of contribution made by thescience and technology towards the process of innovation.As mentioned earlier, the existing measurement tools lacksome key factors of science and technology, such as averagenumber of citations per science and education article, andlocal availability of specialized training and resourcesaffecting innovation rate. Hence the formulation of futurestrategies for enhancing the innovation rate becomes amajor challenge.

Keeping these points in view, the present study has beenconducted to answer questions such as “what is the mostefficient way of measuring innovation rate and technological

capabilities in developing countries?” and “whether there is ascope for further development of an integrated innovationindex that measures the rate of innovation more accuratelythan the existingmethods”. The present study develops a newmeasurement tool of innovation called a technologicalcapability index (TC-index) which is multidimensional andmore effectively accounts for the factors underlying innova-tion and technological capability of developing countries,than the existing methods. Based on the identified factorsaffecting innovation rate derived from TC-index, futuremanagement strategies have been suggested for the achiev-ing a higher growth rate of science and technology andinnovation in developing countries. The TC-index throughits better coverage of underlying development is superior tothe existing indices, and as such it makes a contribution tothe literature.

The rest of this study is organized as follows. Section 2 is areview of the literature on factors affecting the rate ofinnovation and existing measurement methods. The theo-retical framework and methodology in the design of theproposed TC-index is outlined in Section 3. Empirical resultsand its discussion accounting for country group heteroge-neity found in Section 4. Section 5 concludes and providespolicy recommendations.

2. The review of literature

2.1. Factors affecting the rate of innovation

The existing literature on technology and innovationindices used in assessing development is relatively new anddeveloping but yet with major limitations. A number offactors are identified to affect the rate of innovation at bothmicro- and macro-levels. These include firms' innovationability and capacity, industry level collective capability andnetworks, innovation friendly environment, global econom-ic system and trade related intellectual property rightsprotection, state support in interacting learning andtechnological capability, multinational corporation role inorganizational and geographical mobility of innovation,indigenization of learning capability, global and regionalinnovation networks and systems, and coordination be-tween public and private agencies.

The ability to innovate is generally accepted as a criticalsuccess factor to growth and future performance of firms.Carayannis and Provance (2008) investigated how firmscan influence their innovation capacity. The author pro-posed a ‘3P’ construct of innovation measurement at themicro-level, Posture, Propensity and Performance relatedto a firm's innovation capabilities. Schmitz and Strambach(2009) revealed that there exists a fundamental change inthe process of innovation in developed countries. Theyfound that the origin of innovation has been changed fromcentralized system to decentralized mode. It was alsofound that the ‘organizational decomposition of the innova-tion process’ changes the global distribution of innovationof activities, which may also influence innovation in thedeveloping world. Iizuka (2009) by correlating the knowl-edge creation and innovation, suggested that ‘today’ globalenvironment requires different types of knowledge and

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innovative capabilities from the side of the developingcountries.

Results based on firm level data across 43 developingcountries in studying the importance of the innovation systemin developing small economies, revealed that export andimport activities are important channels for the transfer oftechnology and utilization of scale in innovation (Almeida andFernandes, 2008; Fagerberg and Verspagen, 2007). The inter-national agreement has also affected the level of innovationand science and technology in developing countries such asIndia (Dutta and Sharma, 2008). They found that India's signingthe TRIPs (Trade Related Intellectual Property Rights) agree-ment in 1994 affected the Indian firms' growth rate in theirproduct innovation and science and technology, in particularamong the more innovation intensive industrial firms.Komninos and Tsamis (2008) investigated the Greek innova-tion system and identified important asymmetries prevalent inthe system, which explains the low performance of the Greekinnovation system. Oyelaran-Oyeyinka and Gehl Sampath(2009) analyzed the main institutional mechanisms that fosterthe emergence and performance of firms in knowledgeintensive sectors in South Africa and Malaysia. They illustratedthe linkages between interactive learning and technologicalcapabilities, and how state support plays a critical role inenabling these linkages. The development of technologicalcapability has been analyzed byQuadros and Consoni (2009) inthe Brazilian subsidiaries of multinational vehicle assemblers.These played a role in building up local technologicalcapabilities in product development in Brazil. Ernst (2008)explained how innovation off-shoring gives rise to GlobalInnovation Networks (GINs) in the field of electronics industry.They explained about the forces that are responsible for theorganizational and geographical mobility of innovation withinGINs emphasizing their systemic nature.

In a recent article, Ayyagari et al. (2012) studied thefirms' innovation practice in 47 developing countries. Theyextended the definition of innovation to include decisionsources that have impacts on the firms' organizationalactivities and firms' dynamism, and other types of activitiesthat promote knowledge transfer. These include jointventures and obtain new licensing agreements. Frominvestigating the type and the extent of innovationintensity of firms, Egbetokun et al. (2012) found that theeffects of organizational innovation are higher thandiffusion-based innovation in developing countries. Theyconcluded that firms in developing countries are notinnovation-inactive. They may do better if firms are wellorganized and make higher investments in learning andcapacity building.

Some researchers emphasized the link between theinnovation strategies and promotion of science and tech-nology and industrial development. Clark et al. (2008)stressed on the necessity to reduce the barriers of innovationin public sector, and to encourage the drivers of theinnovation in public sector in developing countries bydeveloping technological, social, and financial innovations.In this regard, Kaplinsky (2007) emphasized on the indigeni-zation of learning capabilities to becomepart of comprehensiveinnovation strategy in economic development. The concept ofRegional Innovation System (RIS) developed by Cooke (2008),and a study by Moreira (2008) suggest strategic lines to

achieve a regional innovation strategy to develop innovativeclusters in Portugal. Felker and Sundaram (2007) reported thatMalaysia hasmounted notably comprehensive efforts to build anational innovation system, and despite efforts to promotescience and technology policies, the challenges remainedaffecting successful innovation strategy. They suggested estab-lishment of ordination between public sector technologyagencies and private enterprises.

2.2. A comparison of existing index measures

Several researchers investigated the status of innovationand technology development. However, few studies wereconducted on the role science and technology played inenhancing the rate of innovation. They compute indiceswhich are mainly based on the Human Development Index(HDI) methods of calculation with different dimensions torank countries and to make inference about their state ofscience and technology and polices. Archibugi and Coco(2004) compared several measures of national technolog-ical capabilities. A methodology called ArCo was developedand compared with the methodologies for measuringinnovation and technology development designed by theWorld Economic Forum (WEF), The United Nations Devel-opment Program (UNDP), the United Nations IndustrialOrganization (UNIDO), and the US Research and Develop-ment Corporation (RAND). These methodologies to measuretechnological capabilities are reported in (Archibugi et al., 2009;Desai et al., 2002; Felker and Sundaram, 2007; Lall, 2003; Tidd,2001; U.N., 2008; UNDP, 2001; UNESCO, 2005; UNIDO, 2002;Wagner et al., 2001).

ArCo was estimated for 162 countries by considering threedimensions of technology into account, innovative activity(based on patents registered at US patent office and scientificpublications); technology infrastructure (based on internet,telephonemainlines andmobile, and electricity consumption);and human capital (based on scientific tertiary enrolment,years of schooling, and literacy rate).

The UNDP Technology Achievement Index (TAI) (UNDP,2001) was estimated for 72 countries and considered fourdimensions of technology achievement: Creation of technology(based on patents registered and royalty and license fees),diffusion of newest technologies (based on internet hosts andmedium-and high-technology exports), diffusion of oldesttechnologies (based on telephone mainlines and electricityconsumption), and human skills (based on years of schoolingand tertiary science enrolment).

Science and Technology Capacity Index (Wagner et al.,2001) was developed for 76 countries by RAND Corpora-tion, based on eight indicators, which in turn are aggregat-ed and divided into three categories: Enabling factors(based on GDP and tertiary science enrolment), resources(based on R&D expenditure, number of institutions andscientists and engineers), and embedded knowledge(based on patents, science and technology publications,and co-authored papers). A synthetic index is createdthrough a standardized formula, with different outcomesoccurring according to the weights assigned to the threeindex components.

The global competitiveness index developed by WEF(2001) considered two main measures for competitiveness

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and economic development, the first devoted to the medium-term growth and the second to the short-term currentcompetitiveness index. The growth component was estimatedon the basis of a battery of variables linked to growth groupedinto three components: The level of technology, the qualityof public policies, and the macroeconomic environmentalconditions. The competitiveness index component wasestimated on the basis of variables that concentrate onmicroeconomic aspects, such as the business environmentaround a firm, and the strategy and organization inside acompany. These were estimated for 75 countries, dividedinto two groups, core and non-core, according to the numberof patents produced.

The index by UNIDO (2002) was estimated for 87 countriesconsidering four categories: Technological effort (based onpatents and enterprise financed R&D), competitive industrialperformance (based onmanufactured value added and exportsand medium- and high-technology sectors share), technologyimports (based on foreign direct investment (FDI), foreignroyalties payments, and capital goods), and skills andinfrastructures (based on tertiary technical enrolment andtelephone mainlines).

Few studies suggest improvement of the abovementionedindices. For instance a conceptual framework for approachingthe promotion of technological innovation and its diffusion isdeveloped by Aubert (2005). It took into considerationsconstraints of innovation climates such as poor business andgovernance conditions, low educational levels, and mediocreinfrastructure which affect diffusion of technologies andrelated practices. The author suggested strategies for betterinnovation rate like provision of the necessary package ofsupport related to technical, financial, commercial, and legalaspects, as well as attention to be paid to administrative andcultural traditions. Dolan et al. (2008) explained the process ofhow innovation takes place in the public sector. Two stages inpublic sector innovation, i.e. invention-based and diffusion-based are identified. They also examined three key differencesspecific to the public sector that strongly affect how govern-ment organizations operate in terms of innovations. Crosta andLópez (2009) studied the relationship between innovativeapproaches and eLearning. They measured innovation ineLearning projects, focusing on the concept of innovation andthree key aspects, i.e. technological innovation, sociologicalinnovation, and service customization. Table A.1 in Appendix Aprovides a summary of the comparison between the existingindices.

In sum, though these studies explain several specific factorsthat influence the rate of innovation, they lack requiredparameters to draw an accurate conclusion on the specificfactors affecting the rate of innovation in developing countries.Hence, the present study has been initiated to develop mostinnovative method of measuring innovation.

3. Theoretical framework and methodology

3.1. Technological innovation and indices

National innovative capacity can be defined as the ability ofa country to produce and commercialize a flow of innovativetechnology over the long term. The strength of a nation'scommon innovation infrastructure is affected by the national

innovative capacity and the internal environment forinnovation in firms. It is found that there exists a closerelation between international patenting and variablesassociated with the national innovative capacity framework.Some internal factors like structures, climates, and culturesof organizations influence the innovativeness (Kanter, 1988;Thong, 1999). The management skills, organizational en-couragement for innovation, and support in the form ofinnovation resources would help the improvement ofinnovation (Amabile, 1988). In this regard, Tornatzky andFleischer (1990) suggested that an organization with higherquality (skills) of human resources will have higher abilitiesin technological innovation.

According to Berry et al. (2010), the firms' technologicalinnovation environment can be divided broadly into internaland external parts. The external environment, in which a firmis operating will influence the innovative capability (Handokoet al., 2014). Governmental support is another importantenvironmental characteristic for technological innovation.Governments through effective regulation may play differentroles in the adoptionof innovation (Damanpour, 1991). Accordingto Tornatzky and Fleischer (1990) the environments with highuncertainties have a positive influence on the relationshipbetween organizational structure and organizational innova-tion. The patent protection and regulation system and patentapplication have positive effects on innovation (Mazzoleni andNelson, 1998). In other words, the patent number andinnovation rate are positively correlated. Thus, the patentnumber can be considered as one important factor affecting therate of innovation in developing countries (Chen and Puttitanun,2005; Kim et al., 2012).

Innovation activities in domestic economies may benefitfrom FDI (Fu et al., 2011). These benefits are throughdifferent spillover channels such as reverse engineering,skilled labor turnover, demonstration effects, and suppliercustomer relationships (Cheung and Lin, 2004). The positiveeffect of internet access on the rate of innovation is reportedby Kaufmann et al. (2003). It was found that the internet is anew information and communication technology (ICT) thathas a big potential to improve the relationships andnetworks among various enterprises. ICT will allow firms tointeract with distant partners more easily, and as a conse-quence, innovation networks get enhanced. Hence, ICT use andFDI have been considered as important variables affecting therate of innovation in this study. Based on the theoreticalconsiderations regarding themeaning of innovation and variousfactors affecting innovation, this study develops a moreintegrated approach for developing a new index of innovation,i.e. TC-index.

The above theoretical considerations suggest that severalinternal and external factors affect the rate of innovation indeveloping countries. Though the existing methods addressthe innovation rate to some extent, there is still a scope toimprove the index which will be more integrated in nature.Keeping this in view, the present study has been made todevelop a better technology innovation index. The informa-tion related to the development of science and technologyand innovation in developing countries under study wascollected through a review of literature from standardinternational journals, reference books, and reports. Theliterature was studied and analyzed based on the objective

2 For a detail survey of literature on the use of composite indices in thecontext of development research, see Archibugi et al. (2009) and Heshmati(2003).

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of the study i.e. identifying the level of contribution made bythe science and technology towards innovation in thesedeveloping countries. The existing measures of innovationindiceswere reviewed and their advantages and disadvantageswere assessed.

Based on the pros and cons of existingmethods listed above,the new TC-index was developed which attempts to take intoconsideration of all the prioritized factors affecting scienceand technology and innovation in an integrated manner.According to the score obtained by the TC-index, a newclassification for categorizing the countries based on thepresent level of resources for science and technology is madeavailable with them. It is consistent in reflecting the actualrate of innovation.

3.2. Data and estimation

The strategy for construction and computation of theproposed TC-index is by utilizing data that contains 28indicators (see Table A.2. for the indicators and their datasources) of technology and innovation. The data covers 61developing countries from different continents, observedduring 2003–2008.1 The data is obtained mainly from theWorld Bank's World Development Indicators and otherinternational data sources. The sample is limited to devel-oping countries to focus on the technology innovationcapability of these countries as group, and in line with theobjective of this study and limitations of the literature. Thesample is further distinguished by the level of index intothree groups.

The creation of TC-index indicators is based on severalassumptions. The indicators built in this study share thecommon ground with others, as discussed by Archibugi et al.(2009) the international comparisons are meaningful, regard-less of the differences in social, cultural, and geographicalcontexts. In addition to that, various statistics on technologicalcapabilities can be aggregated based on the assumption thateach individual indicator is a complimentary rather than asubstitute to each other.

The selection of the indictors is mostly based on theliterature of the systems of innovation. However, this study,does not integrate the popular cluster theory into account formany reasons: First, developing countries' lack of statistics onmicroeconomic environment variables. Second, cluster devel-opment has been treated as the ex-ante idea in developedcountries while ex-post in developing countries. This studyrecognizes other group of factors as more relevant todeveloping countries' context. For instance, since most of theadvanced technologies adopted by developing countries arenot locally developed, they are imported from developedcountries, hence, technology import should be included in themodels. The FDI, trade liberalization, and consequently theglobalization led to increase in the capital available todeveloping countries, which potentially increased their abilityto grow (Golub, 2009). One common character of developingcountries recognized bymost authors is domestic technological

1 The current measure of innovation is estimated based on differentindicators of innovativeness that are observed once during the sample period.In other words, the data is a cross sectional dimension, and the identifiedindicators based on data availability are observed at different points of time.

capabilities in these countries (Archibugi et al., 2009). Hence,the indicator “local availability of specialized research andtraining services” is also included. It is worth mentioning thatthe model proposed here does not substantially deviate fromformerly used models to study technological capabilities inOECD countries (see for example: Furman et al., 2002;Gans andHayes, 2006; Morrison et al., 2008; Wennekers, 2006).

The indicators were subjected to a principal componentanalysis (PCA) methodology, using ones as prior communalityestimates. The PCA is defined as amathematical procedure thathelps in transforming a number of correlated variables into asmaller number of uncorrelated variables known as principalcomponents (Kaufmann et al., 2003), so that the first few retainmost of the variation present in all of the original variables(Jolliffe, 2002). The PCA is widely used as a parametricmultivariate indexing to combine several variables into asmaller set of independent variables without losing theessential information from the original data (Tausch et al.,2010).2

The indicators for the rate of innovation are categorized assix principal components. It is the simplest of the trueEigenvector based multivariate analyses. It reveals the internalstructure of the data in a way which best explains the variancein the data. The PCA was applied to extract the components,and this was followed by a varimax or orthogonal rotation.Despite the large number of indicators and multidimensionalnature of the problem, the overall TC-index helps to rank thecountries in one single way, while accounting for thedifferences in the sub-index or principal components. A mainadvantage of the index compared with many other indices isthat it is parametric, and in aggregation of the differentindicators the weight is not selected on an ad-hoc basis, it israther estimated parametrically.

All the six principal components displayed with Eigen-values greater than unity. The results in Fig. 1 which presenta fictitious screed plot from a PCA also suggested that the sixcomponents were meaningful and thus were retained forrotation. Combined, the components 1 and 2 accounted for61.9% of the total variance in the data. Parameters (indica-tors) and corresponding factor loadings (Eigenvectors) arepresented in Tables A.3 and A.4 in Appendix A, respectively.In interpreting the rotated factor pattern, an indicator wassaid to load on a given component if the factor loading was0.30 or greater for that component, and was less than 0.30for the other. Using these criteria, we identify the contrib-uting factors to a principal or sub-index components andtheir contributions by the size and sign of the factor loading.In total, six principal components or sub-indices aredistinguished.3

The principal component 1 (PC1) has 0.3129 weights, PC2has 0.3064, and so on. The cumulative weights for the sixprincipal components form 0.8290, in other words, interpretedas around 83% of the variance in the data can be explained by

3 Some components factor loading are slightly less than 0.3. These are X2(0.296), X7 (0.255), X8 (0.292), X9 (0.281), X12 (0.250), X13 (0.245), X14(0.281), X17 (0.242), X19 (0.275), and X22 (0.292). These components areimportant for the index measure, therefore we used our judgment inconsidering mathematical roundup to include them in the iteration.

Fig. 1. Eigenvalue scree plot.

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the six principal components.Moreover, all Eigenvalues greaterthan unity here is considered. It indicates that PC1 contributesaround 38% of the variation in the rate of innovation which isvery important in deciding the future innovation policies forthe developing countries.

Governments in their policies must emphasize much onthese principal components results in promoting innovation, asit significantly affects the rate of innovation in developingcountries. The rationale for this criterion is straightforward.Each observed variable contributes one unit of variance to thetotal variance in the data set. Any component that displays anEigenvalue greater than unity is accounting for a greateramount of variance than had been contributed by one variable.Such a component is therefore accounting for a meaningfulamount of variance, and is worthy of being retained. Otherprincipal components have little representation of variation ofdata and hence they were excluded.

3.3. Decomposition of TC-index

Table 1 illustrates the six principal components or sub-indices. The results obtained from a PCA applied to the 28indicators (see Table A.2 in Appendix A for detailed definitionof the indicators).

The aggregated TC-index, which is an integrated meth-odology for estimation of innovation rate, is an index formeasuring innovation rate in any nation based on theperformance of different important indicators of scienceand technology, technology diffusion, skilled human capital,research and its outcome, and cross border technologyexchange. Variables such as FDI, internet users, and numberof computers would be completely decided by the nationalpolicy of the governments in developing countries (Fakher,2012; Kahai, 2004). These parameters have significanteffects on the rate of innovativeness and innovation.

Table 1The principal components of sub-indices.

Sub-indices Indicators

PC1 X2, X5, X10, X15, X16, X18, X27, X28PC2 X8, X9, X12, X13, X14, X21, X22, X23, X24PC3 X19, X26PC4 X4, X7, X11PC5 X1, X6, X20PC6 X3, X4, X17

Accordingly, the TC-index would provide a solid advantageover other methods previously used in estimating innova-tion rate.

The TC-index takes into consideration of several weightingfactors related to science and technology, policy making, andhuman capital development in most integrated manner.Though some methodologies were developed for assessmentof innovation rate in developing and developed countries, theyare fragmented in approach. Thus, integration of all theimportant weighting factors is non-systematic and simplistic.For example, the TAI UNDP takes into consideration of severalweighting factors related to technology development, but itlacks the inclusion of human capital development and policymaking indicators which also play significant role in innova-tion. Similarly, the technology index developed by WEF takesinto consideration the level of technology and some policyissues, but it does not cover the human capital developmentand science capacity indicators. The industrial developmentscorecard developed by UNIDO covers mainly the indicatorsof industrial technological aspects. However, it lacks inweighting factors related to science and technology andhuman capital development. The accurate method to assessthe rate of innovation is highly essential for developingcountries, so that they would be in a position to review theirpresent status of innovation, and to develop future strategiesfor enhancing the role of science and technology ininnovation. Technological capability and innovativenesscertainly help them to catch up with the developedcountries. Keeping this in mind, the concept of TC-index hasbeen developed.

TC-index is an integrated innovation index whose scoreranges from negative to zero and to positive values. Therange depends on the way the index is normalized. Theoverall index is a weighted aggregation of six principalcomponents or sub-indices which were further derivedfrom 28 indicators of innovation and technology. As weightsin aggregation of the six components into a multidimen-sional index, we use the share of the total varianceexplained by each principal component. A parametricestimation of the weights and their aggregation based ontheir contribution to the explanation of the variation in thedata, together with a better data quality and coverage,suggest that the index is superior to the existing indexmeasures. TC-index has a broader coverage of severalnumbers of variables which accurately predict the rate ofinnovation. For example, the local availability of specializedtraining and resources, and average number of citations inscience and education journals have been included. HenceTC-index gives an accurate measurement of innovationcompared to the other existing innovation measurementapproaches.

4. Empirical results and discussion

4.1. Analysis of the TC-index

According to the result of the TC-index reported inTable A.5 in Appendix A, among the 61 developing countriesstudied, China as an emerging economy recorded the highestTC-index value (3.19) which reflects its highest potential forinnovation in science. China was followed by Estonia (1.25)

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and Malaysia (1.02) in terms of TC-index value, reflectingthe scope of higher status of innovation in science andtechnology. On the other hand, the lowest TC-index value(−0.56) was recorded for Iran, due to the lower values ofpatent, FDI, literacy, and education indicators. Bangladesh,Tadzhikistan, and Cambodia also registered lowest level ofTC-index value which shows that these countries have lessfavorable conditions for innovations.

One important aspect of this study is that the innovationrate by TC-index was more decided by the specific factors suchas patent, science and engineering and FDI indicator, whereasthe previous methods mostly stressed on HDI. As it covers theonly three broad factors (life expectancy, year of schooling andGDP per capita) affecting innovation, HDI may not beconsidered as accurate method of estimating innovation ratelike TC-index. Equal weights were given to the three compo-nents in HDI without providing any justification. We bench-mark theHDI as an illustrative to comparewith the TC-index. Interms of HDI value, Estonia leads the table followed by Chinaand Malaysia.

Though China ranks first in TC-index value, it rankssecond in HDI value. This is due to the fact that the rate ofinnovation is more determined by the specific determinantsused in our study, which decides the rate of innovationbetter than the HDI, where the general factors for humandevelopment are involved. For example, the diffusion oftechnology and innovation is facilitated more by specificparameters like patenting (Bessen and Maskin, 2009;Scotchmer and Green, 1990), whereas HDI is mainly affectedby health and literacy, leading to the differences in values ofHDI and TC-index for the same nation.

China again leads in the specific indicators' contributionthat determines the TC-index value. However, as far as thegeneral factors for human resource development are taken intoconsideration, the effect has not been that good relative toother developing countries. Estonia recorded as a first rank inHDI where as it ranked second in TC-index value. Thisdifference is due to the variation between the specificindicators affecting the rate of innovation such as patents,internet, computers, scientific journal access, and FDI (one ofthe main sources of technology transfer). These indicatorsare the main consideration for TC-index value and thegeneralized parameters such as literacy and education asmain components of HDI. Malaysia ranked third both in HDIand TC-index. HDI seem to be a good indicator of bothhuman development and innovative capacity of developingcountries. As it is revealed from several primary andsecondary sources of literature that the rate of innovationis more in China than Estonia and Malaysia and otherdeveloping countries, hence TC-index can be considered asmore accurate method than previous methods of measuringinnovation in developing countries.

According to Foster et al. (2012), highly positive corre-lations among component variables are favorable, as theycan enhance rank robustness. The correlation matrix re-vealed that the highest correlation was found between pairsof majority of the indicators (see Tables A.6 and A.7 inAppendix A for details). Some indicators like X6, X7, X9, X11,X12 and X13 did not show strong correlation with othervariables. The strong correlation between HDI and averagenumber of citations per science and education article, FDI

inflows, FDI outflows and gross secondary enrollment rate isdue to the significant allocation of resources in terms of FDItowards the secondary enrollment and scientific journalpapers, which is directly proportional to the innovation rateand science development.

Similarly, the high correlation between the local avail-ability of specialized training and resources with that of FDItowards the secondary enrollment and scientific journalpapers is due to the direct proportional relationshipbetween training at local level and science developmentand FDI, which is also encouraging innovation development.The correlation between the adult literacy rate and grossenrollment ratio is due to the interdependency of both ofthese factors on each other.

The high correlation between the number of computersand the internet connectivity is also due to interdependencyon each other. Moreover, the correlation between thehuman development ratio and gross secondary enrolmentratio is due to the fact that the literacy is one of the mainindicators of the human development ratio for estimating itsnet value. The positive correlation between the localavailability of average number of citation in Science andEducation journals and FDI inflows is due to the higherextent of investment towards the development of scientificjournals, which is directly proportional to the innovationrate (Vinkler, 2008).

We report in Table A.7 in Appendix A the Pearsoncorrelation coefficients for the TC-index and the principalcomponents along with the HDI, to show the extent ofcorrelation among the components and the overall TC-indexand HDI.

4.2. Country group heterogeneity

The developing countries under study have been classifiedinto three distinct groups based on the overall TC-index scoreas follows.

Group 1: Consists of scientifically high potential countries,where the TC-index score is greater than (0.50). Only sixcountries are listed under this group begin from China andended with Croatia. It is worth mentioning that only onecountry from the Middle East is included in this group, theUnited Arab Emirates (UAE). The UAE government shouldestablish a national innovation plan, policy, council, andsupport program with more attention given to those factorsthat enhance the technological capabilities. It should providespecific reforms to improve its national competitivenessthrough innovation in different areas related to technologicalcapabilities. Also to build a strong domestic innovation base,tackling new technological changes and competitive chal-lenges. This will enable the UAE economy to depend on theperformance of its national innovation system and itsinnovation diffusion. For the whole group, althoughliteracy percentage is remarkable, the efforts must bemade by policy makers to elevate the status of research,by spending higher share of the state budget on R&D, andby encouraging scientists for filing more number of patentapplications.

Group 2: Consists of scientifically moderate potentialcountries, where the TC-index score ranges from (0.0) to(0.50). Sixteen countries were categorized under this group,

217N.T. Khayyat, J.-D. Lee / Technological Forecasting & Social Change 92 (2015) 210–223

starting with Latvia and ended with Ukraine. According toCunska et al. (2013), Latvia's GDP per capita grew significantlyafter the end of the pre-transition crisis in the early 1990s.Between 1993 and 2007 prosperity levels increased by roughly250%, equivalent to an annual growth rate of more than 7.5%.The crisis of the last few years has reduced prosperity levels bymore than 20%, pushing real living standards back to the levelof 2005. However, Latvian innovation infrastructure accordingto the Cunska et al. (2013) is poor. It is recommended forcountries like Latvia (and other countries within the Group 2)to take action toward improving the quality of educationsystem. Policy makers should emphasize on enhancing theliteracy level and education enrollment shares in thesecountries. The government policy to eradicate povertywould help in this regard, as several people do not getaccess to education due to the cost of involvement.Furthermore, to improve quality, the higher education inthese countries should open up to international competition.Moreover, the policy making should facilitate higher budgetfor research institutes and laboratories. The vocationaltraining reform should aim at developing a genuineapprenticeship system, in which employers will be directlyinvolved in training to ensure that training matches theneeds of employers.

Group 3: Labeled as scientifically low potential countrieswhere the TC-index score is negative. Thirty nine countrieswere categorized under this group, starting from Brazil andending with Iran. Despite the vast economic reforms inBrazil during 1990s, the evolution of the national innovationsystem was slow, due to lack of active strategies in theindustrial and technological areas to face the decrease inproductivity growth rates and output stagnation (Guennif andRamani, 2012). Hence, radical regulatory changes will havepositive effects, andmay generate positive externalities similarto radical technological discontinuities. In general, this group ofcountries is far behind. The intensified efforts must be made toenhance the literacy share and education enrollment percent-age, and to reduce the unemployment and poverty rates. Thepolicy making should allocate a higher amount from the statebudget for university education and schemes for encouragingscientists for filing more number of patents. Continuous andheavy investments in human capital enhanced by educationand training enhance a competitive environment undergovernment coordination with consolidation of active institu-tions that are essential to drive the knowledge intensivesectors.

5. Conclusion and recommendations

The present study developed a new measurement tool,the TC-index, to analyze the level of innovation fordeveloping countries. The TC-index was estimated for 61developing countries around the world observed during2003–2008. The highest growth rate of innovation wasnoticed in China, followed by Estonia and Malaysia amongthe developing countries under study. The lowest innova-tion rate was reported in Iran, Bangladesh, Tadzhikistan, andCambodia.

The differences existed in the ranking of some countrieslike China when we compare HDI and TC-index values. Thisis due to the aggregation of several general factors or

indicators for estimating HDI, where specific indicators ofinnovation were considered in computation of the TC-index.Based on the results, a number of recommendations are tobe made. These are aimed at enhancing the rate ofinnovation, competitiveness and development in thesecountries. The bulk of policy recommendations are asfollows.

First, the specific indicators which determine the rate ofinnovation more accurately such as (X1, X2, X5, X6, X8, X12,X14, X15, X25, X26, X27, and X28) have to be better reflectedand focused on well in the national policy of all thedeveloping countries under study. The combination shouldbe determined by the initial condition of the individualcountry.

Second, emphasis must be paid to allocate significantproportion of their annual budgets towards the scienceeducation (X11), gross enrollment rate (X18) and internetconnectivity (X8, X9, and X23). These factors enhancetechnological capability and facilitates the faster rate ofinnovation in developing countries, by identifying andaccessing for their local needs, and ability adaptable foreigntechnology and technology information (Easterby-Smith andPrieto, 2008).

Third, the policy of national awards for the scientists andresearchers who make sound breakthroughs in science andtechnology be established. This will be useful in promotingresearchers to get motivated and encouraged to do moreresearch for generating innovative technologies in developingcountries.

Fourth, the international relations with other countriesmust be bettered in the social, economic, cultural andscientific spheres. All of the spheres are quite interrelatedand correlated in affecting the fate of the FDI flowing in tothe domestic educational sector. FDI facilitates the innova-tion spillovers by increasing different types of technologiesand management practices brought by foreign firms (Zhanget al., 2010).

Fifth, the efforts must be made sincerely towards themodification of school curriculum and syllabus, so thathigher emphasis is given to the creativity and spontaneity ofthe children and problem solving exercises, develop behav-iors, skills, and attributes that help to create innovativepractices and cope with changes (Seikkula‐Leino, 2010).This would make impeccable effect on the mindset of thechildren, who may opt for the field of research in future,which in turn would certainly lead to higher rate ofinnovation.

Sixth, the companies or corporate houses must be encour-aged in forms of relaxation of considerable portion of corporatetaxes for developing an innovative way of product andproduction processes, which are environmentally friendly andeconomically viable. Firms come up with most innovativeproduction processes may be given a tax holiday incentive forspecific period, so that other firms would also be motivated todevelop new practices.

Finally, the special focus must be given to the encour-agement of local organizations to conduct the specializedtraining programs to promote innovation activities, innova-tion cooperation within and between public and privateorganizations and their foreign collaborators in R&Dactivities.

Table A.1Comparison of existing measurement indices of technology and innovation.Source: Authors' collection

No Existingmeasurement index

Developed by Methodology Advantages Disadvantages No. ofcountries

1 TechnologyAchievementindex

UNDP (2001) Technology creation was assessed throughnational patents and payment of royalty

Assessment of technologicaladvancement

Measurement of innovationas part of technologydevelopment is meager

72

2 Science andtechnologycapacity index

RANDWagner et al.(2001)

Technology index was computed basedon patents at USPTO and enterprisesfinanced Research & Development

Focus on science andtechnology development

Not much emphasis oninnovation

76

3 Technology index WEF (2001) Innovation sub index was computedbased on patents at USPTO, survey dataand tertiary enrolment

Assessment of technologicaladvancement

Measurement of innovationas part of technologydevelopment is meager

75

4 Industrialdevelopmentscore board

UNIDO(2002)

Technology creation was assessed throughpatents at USPTO, international scientificjournal articles and allocation towardsscientific research and development

Focus on industrialdevelopment

Not much emphasis onscience and technology

87

5 ArCo Archibugiand Coco(2004)

Technology creation was assessed throughpatents at USPTO and internationalscientific journal articles

Focus on technology andindustrial development

Not much emphasis oninnovation

162

6 Conceptualframework forinnovation

Aubert(2005)

Developed conceptual framework basedon problems of diffusion of innovation indeveloping countries

Focus on identification ofproblems for innovation indeveloping countries anddevelopment of effectivestrategies and conceptualframework

Science and technology rolein innovation was notspecifically studied.More generalized approach

N15

7 Innovation ingovernmentorganizations,public sectoragencies andpublic sector NGOs

Dolan et al.(2008)

National Endowment for Science andTechnology and Arts followedmethodology for computing innovationindex in public sector agencies which iscomposed of 54 indicators belonging to10 different dimensions

Innovation index was developedfor government organizationsand identified both inventionbased and diffusion basedinnovations in public sector

Focus on public sectorenterprises only Lacksapplication for privateorganizations

N12

8 i-AFIEFL Crosta andLópez (2009)

i-AFIEFL methodologyInnovative Approaches for full inclusionin e-learning

Focus on sociological innovation,technological innovation andservice customization

Only applicable fore-learning only

3

9 TC-Index Current study Estimated by taking in to considerationof six principals i.e. patent and journalindex, science and education index), FDIindex, researcher and technician indexand computer and internet.

Takes in to consideration of mostspecific parameters or factorsaffecting the process ofinnovation and technologycompared to any existingmethod of measuring innovation.

The computation of FDIIand II may not reflect thestatus of innovation insome countries where theirpractice is not common.

61

Table A.2The indicators of TC-index and data sources.

No. Label Indicator Source

1 X1 Patents Granted by USPTO/Mil. People,avg 2003–2007

United State Patent and Trademark Office: available at: http://www.uspto.gov/about/stats/index.jsp#

2 X2 Patents Granted by USPTO, avg 2003–2007 United State Patent and Trademark Office, available at: http://www.uspto.gov/about/stats/index.jsp#3 X3 International Internet Bandwidth (bits

per person), 2007World Bank, World Development Indicators and EconStats, available at: http://www.econstats.com/wdi/wdiv_606.htm

4 X4 Intellectual Property Protection (1–7),2008

World intellectual property indicator (WIPO), available at: http://www.wipo.int/ipstats/en/

5 X5 Foreign direct investment, net inflows(BoP, current US$)

United State Patent and Trademark Office: available at: http://www.uspto.gov/about/stats/index.jsp#

6 X6 Public Spending on Education as % of GDP,2007

World Development Indicator by the World bank, available at: http://data.worldbank.org/products/data-books/WDI-2007

7 X7 Availability of e-Government Services, 2008 United Nations E-Government Survey, available at: http://unpan1.un.org/intradoc/groups/public/documents/un-dpadm/unpan038848.pdf

8 X8 Internet Users per 1000 People, 2007 United Nations E-Government Survey, available at: http://unpan1.un.org/intradoc/groups/public/documents/un-dpadm/unpan038848.pdf

9 X9 Internet Access in Schools, 2008 World Development Indicator by the World bank, available at: http://data.worldbank.org/indicator/IT.NET.USER.P2

0 X10 Human Development Index, 2008 Human Development Report 2009, available at: http://hdr.undp.org/en/content/human-development-report-2009

Appendix A

218 N.T. Khayyat, J.-D. Lee / Technological Forecasting & Social Change 92 (2015) 210–223

Table A.2 (continued)

No. Label Indicator Source

1 X11 Quality of Science and Math Education,2008

World Development Indicator by the World bank, available at: http://data.worldbank.org/indicator/IT.NET.USER.P2

2 X12 S&E Journal Articles / Mil. People, 2005 World Development Indicator by the World bank, available at: http://data.worldbank.org/indicator/IT.NET.USER.P2

3 X13 GDP per Capita (in/l current $ PPP), 2007 World Development Indicator by the World bank, available at: http://data.worldbank.org/indicator/IT.NET.USER.P2

4 X14 Computers per 1000 People, 2007 United Nations E-Government Survey, available at: http://unpan1.un.org/intradoc/groups/public/documents/un-dpadm/unpan038848.pdf

5 X15 Local availability of specialized researchand training services, 2008

The Global Competitiveness Report (GCR) by the World Economic Forum, available at: http://www.weforum.org/reports/global-competitiveness-report-2013-2014

6 X16 Average number of citations per S&Earticle, 2005

Special tabulations: Average number of citations per S&E article, 2005 Citation counts from set ofjournals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI).

17 X17 Adult Literacy Rate (% age 15 and above),2007

World Development Indicator by the World bank, available at: http://data.worldbank.org/indicator/IT.NET.USER.P2

18 X18 Gross Higher Education Enrollment rate,2007

World Development Indicator by the World bank, available at: http://data.worldbank.org/indicator/SE.TER.ENRR

19 X19 Exports of Goods and Services as % of GDP,2007

World Development Indicator by the World bank, available at: http://data.worldbank.org/indicator/IT.NET.USER.P2

20 X20 High-Tech Exports as % of Manuf. Exports,2007

World Development Indicator by the World bank, available at: http://data.worldbank.org/indicator/TX.VAL.TECH.MF.ZS

21 X21 Mobile Phones per 1000 People, 2007 United Nations E-Government Survey, available at: http://unpan1.un.org/intradoc/groups/public/documents/un-dpadm/unpan038848.pdf

22 X22 Total Telephones per 1000 People, 2007 United Nations E-Government Survey, available at: http://unpan1.un.org/intradoc/groups/public/documents/un-dpadm/unpan038848.pdf

23 X23 Internet users (per 100 people) United Nations E-Government Survey, available at: http://unpan1.un.org/intradoc/groups/public/documents/un-dpadm/unpan038848.pdf

24 X24 Mobile cellular subscriptions (per 100people)

United Nations E-Government Survey, available at: http://unpan1.un.org/intradoc/groups/public/documents/un-dpadm/unpan038848.pdf

25 X25 S&E Journal Articles, 2005 Science and Engineering Indicator: 2006, provided by National Science Board NSF, available at: http://www.nsf.gov/statistics/seind06/

26 X26 S&E articles with foreign co-authorship(%), 2005

Science and Engineering Indicator: 2006, provided by National Science Board NSF, available at: http://www.nsf.gov/statistics/seind06/

27 X27 FDI Inflows as % of GDP, 2003-07 United Nations Conference on Trade and Development UNCTAD Report, available at: http://unctad.org/SearchCenter/Pages/Results.aspx?k=FDI%20Inflows%20as%20%25%20of%20GDP

28 X28 FDI Outflows as % of GDP, 2003–07 United Nations Conference on Trade and Development UNCTAD Report, available at: http://unctad.org/SearchCenter/Pages/Results.aspx?k=FDI%20Inflows%20as%20%25%20of%20GDP

Table A.3Parameters (indicators) and Eigenvectors (factor loading).

Indicators PC 1 PC 2 PC 3 PC 4 PC 5 PC 6

X1 −0.016 0.248 −0.038 0.130 0.429 0.014X2 0.296 0.042 −0.247 0.054 0.062 −0.019X3 −0.032 0.164 0.174 −0.162 −0.104 0.548X4 0.021 0.223 −0.056 0.383 −0.298 0.055X5 0.317 0.053 −0.090 −0.028 0.030 −0.023X6 −0.094 −0.008 −0.029 0.180 0.238 0.598X7 −0.084 0.185 −0.200 0.255 −0.251 0.116X8 −0.072 0.292 0.056 −0.152 0.179 0.128X9 0.052 0.281 −0.044 0.271 −0.167 −0.039X10 0.333 0.029 0.086 −0.012 0.001 −0.007X11 0.040 0.171 −0.053 0.469 −0.205 −0.033X12 −0.043 0.250 −0.105 −0.123 0.042 0.233X13 −0.068 0.245 0.033 −0.194 −0.095 −0.212X14 −0.051 0.281 0.082 −0.077 0.222 −0.005X15 0.334 0.028 0.086 −0.011 0.000 −0.005X16 0.333 0.027 0.091 −0.009 0.006 0.012X17 −0.195 0.142 0.040 −0.144 0.136 −0.242X18 0.321 0.075 0.079 −0.056 0.009 −0.058X19 −0.117 0.191 0.275 0.173 0.007 −0.152X20 −0.033 0.088 −0.013 0.384 0.566 −0.248X21 −0.101 0.284 0.036 −0.151 −0.190 −0.130X22 −0.102 0.292 0.024 −0.186 −0.122 −0.132X23 0.190 0.249 0.113 −0.090 0.164 0.141X24 0.024 0.314 0.061 −0.105 −0.088 −0.022X25 0.017 0.028 −0.678 −0.091 0.053 0.028X26 −0.086 −0.162 0.476 0.218 −0.030 0.061X27 0.332 0.034 0.095 −0.008 −0.009 0.001X28 0.333 0.030 0.089 −0.011 −0.002 0.000

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Table A.5The TC-index compared with HDI.

Country TC-index HDI Country TC-index HDI

Rank Value Rank Value Rank Value Rank Value

China 1 3.192 12.465 2 Georgia 32 −0.16 6.136 37Estonia 2 1.252 14.726 1 Venezuela 33 −0.166 6.667 29Malaysia 3 1.027 12.128 3 Indonesia 34 −0.167 5.993 39Lithuania 4 0.566 11.194 5 Senegal 35 −0.181 5.268 47UAE 5 0.563 12.055 4 Peru 36 −0.192 6.203 35Croatia 6 0.536 10.41 6 Botswana 37 −0.196 6.126 38Latvia 7 0.478 9.569 7 Kyrgyz 38 −0.206 5.78 41Costa Rica 8 0.468 9.066 10 Lesotho 39 −0.226 5.73 42Jamaica 9 0.313 9.087 9 El Salvador 40 −0.245 6.336 31Kuwait 10 0.297 9.421 8 India 41 −0.249 6.446 30Thailand 11 0.213 8.882 11 Kenya 42 −0.254 5.348 45Chile 12 0.203 8.85 12 Ecuador 43 −0.26 6.16 36Tunisia 13 0.148 7.911 17 Egypt 44 −0.263 5.217 48Saudi Arabia 14 0.121 8.451 14 Armenia 45 −0.287 4.988 50Mauritius 15 0.121 7.858 18 Guatemala 46 −0.294 5.636 43Panama 16 0.119 8.663 13 Burkina Faso 47 −0.326 4.191 56South Africa 17 0.076 7.807 20 Bolivia 48 −0.328 5.407 44Mexico 18 0.061 7.721 21 Paraguay 49 −0.33 5.896 40Philippines 19 0.046 7.045 27 Algeria 50 −0.338 5.27 46Uruguay 20 0.021 7.697 22 Pakistan 51 −0.349 4.177 57Jordan 21 0.014 7.077 26 Nicaragua 52 −0.367 5.007 49Ukraine 22 0.009 7.984 16 Uganda 53 −0.381 4.513 52Brazil 23 −0.006 8.208 15 Cameroon 54 −0.381 4.405 53Oman 24 −0.008 7.628 23 Ethiopia 55 −0.388 3.713 60Argentina 25 −0.018 7.837 25 Madagascar 56 −0.39 4.338 55Morocco 26 −0.048 6.323 32 Mozambique 57 −0.401 3.874 59Colombia 27 −0.049 7.137 25 Cambodia 58 −0.413 4.377 54Kazakhstan 28 −0.087 6.978 28 Tajikistan 59 −0.415 4.582 51Mongolia 29 −0.121 6.29 33 Bangladesh 60 −0.507 3.455 61Vietnam 30 −0.132 6.215 34 Iran 61 −0.56 3.93 58Turkey 31 −0.154 7.351 24

Table A.6Pearson correlation coefficient for the TC-index and its sub-indices.

PC1 PC2 PC3 PC4 PC5 PC6 HDIPC1 1PC2 0 1 0PC3 0 0 1PC4 0 0 0 1PC5 0 0 0 0 1PC6 0.906 0.291 0.237 0.166 0.103 1HDI 0.985 0.059 −0.051 −0.013 0.077 0.904 1

Table A.4Eigenvalues of the correlation matrix (factor loading weighted parameters).

Principals Eigen value Difference Proportion Cumulative

PC1 8.762 0.183 0.312 0.312PC2 8.578 6.767 0.306 0.619PC3 1.811 0.152 0.064 0.684PC4 1.658 0.344 0.059 0.743PC5 1.313 0.225 0.046 0.790PC6 1.088 0.200 0.038 0.829

220 N.T. Khayyat, J.-D. Lee / Technological Forecasting & Social Change 92 (2015) 210–223

Table A.7The correlation matrix of the indicators of innovation for 61 developing countries.

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12

X1 1X2 0.115 1X3 0.233 −0.074 1X4 0.459 0.177 0.243 1X5 0.057 0.865 −0.05 0.123 1X6 0.111 −0.221 0.046 0.042 −0.238 1X7 0.342 −0.064 0.328 0.576 −0.075 0.068 1X8 0.648 −0.103 0.565 0.367 −0.072 0.079 0.379 1X9 0.501 0.237 0.307 0.729 0.271 −0.098 0.55 0.577 1X10 0.004 0.834 −0.039 0.094 0.927 −0.26 −0.225 −0.138 0.204 1X11 0.316 0.201 0.048 0.597 0.121 0.02 0.31 0.261 0.728 0.135 1X12 0.609 −0.061 0.396 0.349 −0.015 0.072 0.371 0.62 0.562 −0.075 0.351 1X13 0.473 −0.11 0.204 0.43 −0.088 −0.031 0.321 0.621 0.439 −0.116 0.17 0.47X14 0.699 −0.052 0.348 0.408 −0.039 0.009 0.306 0.828 0.595 −0.073 0.301 0.71X15 0.001 0.834 −0.04 0.093 0.926 −0.258 −0.226 −0.142 0.201 1 0.135 −0.077X16 0.008 0.834 −0.018 0.091 0.926 −0.257 −0.213 −0.14 0.196 0.998 0.122 −0.077X17 0.31 −0.443 0.202 0.03 −0.409 0.055 0.252 0.486 0.217 −0.509 0.04 0.343X18 0.081 0.819 0.01 0.148 0.927 −0.276 −0.165 −0.01 0.3 0.977 0.164 0.017X19 0.404 −0.259 0.292 0.417 −0.294 0.094 0.313 0.508 0.41 −0.245 0.309 0.245X20 0.514 0.022 −0.048 0.144 −0.012 0.08 0.226 0.249 0.26 −0.082 0.186 0.026X21 0.444 −0.198 0.412 0.466 −0.132 −0.035 0.526 0.734 0.583 −0.204 0.291 0.572X22 0.49 −0.203 0.41 0.427 −0.128 −0.04 0.496 0.791 0.585 −0.208 0.284 0.626X23 0.542 0.535 0.429 0.369 0.621 −0.111 0.135 0.659 0.611 0.627 0.329 0.467X24 0.589 0.131 0.449 0.565 0.211 −0.051 0.424 0.755 0.698 0.161 0.4 0.632X25 0.06 0.427 −0.056 0.018 0.179 0 0.213 0.052 0.072 −0.049 0.04 0.134X26 −0.33 −0.42 −0.039 −0.267 −0.377 0.087 −0.173 −0.359 −0.355 −0.22 −0.145 −0.427X27 0.009 0.827 −0.018 0.109 0.923 −0.264 −0.216 −0.127 0.222 0.998 0.149 −0.06X28 0.006 0.834 −0.027 0.099 0.926 −0.258 −0.22 −0.136 0.206 0.999 0.135 −0.073

X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23 X24 X25 X26 X27 X28

X13 1X14 0.681 1X15 −0.119 −0.076 1X16 −0.125 −0.072 0.998 1X17 0.376 0.43 −0.514 −0.518 1X18 −0.011 0.042 0.975 0.971 −0.348 1X19 0.504 0.493 −0.248 −0.247 0.457 −0.169 1X20 0.006 0.258 −0.083 −0.076 0.217 −0.052 0.279 1X21 0.721 0.619 −0.208 −0.212 0.559 −0.064 0.54 0.107 1X22 0.735 0.682 −0.212 −0.216 0.604 −0.057 0.535 0.121 0.983 1X23 0.354 0.605 0.625 0.624 −0.025 0.7 0.224 0.157 0.394 0.431 1X24 0.699 0.688 0.157 0.153 0.343 0.286 0.458 0.133 0.853 0.846 0.702 1X25 0.015 0.01 −0.049 −0.049 0.024 −0.023 −0.233 0.007 0.003 0.03 −0.008 0.011 1X26 −0.354 −0.242 −0.222 −0.203 −0.033 −0.304 0.047 −0.012 −0.343 −0.376 −0.427 −0.421 −0.567 1X27 −0.116 −0.062 0.998 0.996 −0.496 0.978 −0.232 −0.087 −0.196 −0.199 0.635 0.167 −0.061 −0.224 1X28 −0.113 −0.077 0.999 0.998 −0.51 0.976 −0.239 −0.083 −0.202 −0.207 0.629 0.163 −0.05 −0.222 0.998 1

221N.T. Khayyat, J.-D. Lee / Technological Forecasting & Social Change 92 (2015) 210–223

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Nabaz T. Khayyat received PhD in Economics, Swiss Management University,and PhD in Engineering, Seoul National University. He is an EngineeringEconomist, who worked for the UN for several years, as well as differentinternational humanitarian organizations, and recently held a positionwith theKurdistan Regional Government KRG as an Information Management Advisorin the Ministry of Agriculture and Water Resources. His research field includesEnergyEconomics, Industrial Dynamics, FactorDemandModels and ProductionRisk, and Technological Demand forecasting.

Jeong-Dong Lee (PhD, Seoul National University) is an Engineering Economistwho is currently a full professor and anAssociate Deanof College of Engineeringat Seoul National University. His research field includes Data EnvelopmentAnalysis (DEA), Innovation Theory and Industry Dynamics.