intellectual capital and national innovation systems performance
TRANSCRIPT
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Knowledge-Based Systems xxx (2014) xxx–xxx
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Contents lists available at ScienceDirect
Knowledge-Based Systems
journal homepage: www.elsevier .com/ locate /knosys
Intellectual capital and national innovation systems performance
http://dx.doi.org/10.1016/j.knosys.2014.08.0010950-7051/� 2014 Elsevier B.V. All rights reserved.
⇑ Corresponding author. Tel.: +886 2 2898 6600x604981; fax: +886 2 2898 5927.E-mail addresses: [email protected] (W.-M. Lu), [email protected]
(Q.L. Kweh), [email protected] (C.-L. Huang).1 Tel.: +886 2 2898 6600x604983; fax: +886 2 2898 5927.
Please cite this article in press as: W.-M. Lu et al., Intellectual capital and national innovation systems performance, Knowl. Based Syst. (2014),dx.doi.org/10.1016/j.knosys.2014.08.001
Wen-Min Lu a,⇑, Qian Long Kweh b, Chia-Liang Huang a,1
a Department of Financial Management, National Defense University, No. 70, Section 2, Zhongyang North Road, Beitou, Taipei 112, Taiwanb Department of Accounting, College of Business Management and Accounting, Universiti Tenaga Nasional, Sultan Haji Ahmad Shah Campus, 26700 Muadzam Shah, Pahang, Malaysia
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a r t i c l e i n f o
Article history:Received 15 April 2014Received in revised form 1 July 2014Accepted 2 August 2014Available online xxxx
Keywords:National innovation systemNetwork DEAIntellectual capitalPerformance evaluation
a b s t r a c t
Innovation is a key resource for the well-being of national economies and international competitiveadvantages. First, this study develops a network data envelopment analysis (DEA) production processto evaluate the R&D efficiency and economic efficiency of the national innovation system (NIS) in 30countries. Our findings show that the R&D efficiencies of the NIS are better than the economic efficien-cies. Second, this study examines the effect of intellectual capital (IC) on the NIS performance throughtruncated regression. Our findings indicate that IC does play an important role in affecting the NIS per-formance. Finally, this study presents a managerial decision-making matrix and makes suggestionsthrough a performance improvement strategy map to help government and managers improve the NISperformance.
� 2014 Elsevier B.V. All rights reserved.
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1. Introduction Since the seminal work of Freeman and Christopher [29], 6263
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In the survey of 2007 global industrial technology innovationpolicy development, the Science, Technology and Industry Score-board of Organization for Economic Co-operation and Develop-ment (OECD) finds that a large majority of OECD countriesemphasize technological system and innovation because thesetwo factors affect not only domestic and international competition,but also economic growth [61]. A technological system is thesystem that affects specific technology or industry. According toCarlsson and Stankiewicz [13], technological system is a dynamicnetwork among economic entities in a specific technological areathat aim to create, transmit, and apply technology. In contrast, anational innovation system (NIS) affects the overall developmentof innovation. That is, a NIS is the network of institutions in thepublic and private sectors whose activities and interactions initi-ate, import, modify, and diffuse new technologies [28]. Technolog-ical system differs significantly from NIS on the following threeperspectives. First, technological system emphasizes creations ofnew technology, while NIS focuses on the transmission and appli-cation of technology. Second, the strength of technological systemin each technological area differs significantly within a country.Third, the classification of technological system is based on tech-nology rather than national boundaries.
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which studies Japanese technological development, NIS researchhas evolved into an important research agenda. NIS is formedby the interaction among innovation creation, transmission, andapplication of various institutions. In other words, NIS is animportant contributor for economic growth when the interac-tions among various institutions are well organized. NIS not onlyoffers recommendations on innovation planning and competi-tiveness enhancement on technological front to policy makers,but also attracts the attention of researchers in the field of eco-nomic and innovation. The establishment of NIS thus becomesmore important because the interaction among government,research institution, academia, and industry could result in com-mercialization of technological research outputs, which in turnwould increase national competitiveness.
The innovation of a country or an industry does not originatefrom a single economic entity; rather, it is a combination of a com-posite and systematic mechanism. According to OECD [60], thedevelopment of technology and innovation is the outcome of theinteraction among corporations, academia, and governmentresearch institution. The knowledge and technology possessed bythe personnel and institutions are the key factors in the innovationprocess. The development of NIS is to improve the network relation-ship among the system members, which leads to enhancement inthe overall national innovative capability [59]. As noted earlier,the NIS performance is the primary determinant of national compet-itiveness. A holistic assessment that considers overall industrialeffects would enable a greater understanding on the impact of NISon national competitiveness. Besides, this can be guidance for tech-nological policies and research resource allocation in a country.
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To determine its own global positioning, a country can obtainthe recent NIS performance of other countries from the historicaldata of NIS development. However, it remains a challenge to eval-uate NIS, which is characterized by multiple dimensions, especiallyin a cross-country setting. To dates, there is no consensus about theNIS measurement. The common problems include the lack of con-sensus in the measurement model, ambiguous measurement indi-cators, and the lack of an effective holistic measurement model.These situations suggest that there is a critical need for a moreobjective and reliable measurement to express the relative effi-ciency of cross-countries NIS.
To ensure proper decision making, the performance of an insti-tution needs to be measured using multiple indicators [16]. Moststudies have recommended the use of multi-factor performancemeasure model to measure organizational operating performance[2,4,6]. Data envelopment analysis (DEA) is an approach thatsimultaneously evaluates multiple inputs and multiple outputsvia linear programming technique to determine the productionfrontier as a basis of efficiency measure [3,33]. Traditional DEAmodels, however, ignore intermediate measures or interconnect-ing activities in a production process. Therefore, this study doesnot apply the traditional DEA that ignores the connectivity of inter-nal economic activities. Set in a slightly different research setting,this study employs a network DEA model that is able to explore the‘black-box’ process of NIS in estimating the performance of NIS, theR&D and economic efficiencies.
The comprehensiveness of NIS cannot be assessed merelythrough the performance. Other measurement methods shouldbe employed to provide an overall assessment on NIS. In this glob-alized knowledge-based economy, knowledge, an important factorof production, has gradually replaced machinery, equipment, cap-ital, raw materials, and labors. The concept of intellectual capital(IC) dates back to the work of Galbraith [31], where IC is inter-preted as the difference between market value and book value.Stewart [72] suggests that IC can be deemed as a major source ofenhancing or creating competitive advantage for an institutionand it is also the most important resource of overall value creation.
Many scholars employ Tobit regression to examine the impactof a variety of external environmental factors on organizationaloperating efficiency [8,18,64,65,74,78]. Simar and Wilson [71],however, utilize truncated regression with bootstrapping approachand find that truncated regression provides greater validity thanTobit regression. Consistently, this study employs Simar and Wil-son [71] truncated regression to investigate the impact of IC onthe NIS performance of 30 countries.
The purposes of this study are threefold. First, we develop a net-work DEA model that comprises R&D efficiency and economic effi-ciency to measure the NIS performance. Second, we examine therelationship between IC and NIS performance using truncatedregression with the aim to provide recommendations for improv-ing NIS performance. Third, we construct a decision-making matrixand a performance improvement strategy map to assist countriesto enhance their competitiveness through the integration of thenetwork efficiency and IC dimensions.
The remaining sections of this study are organized as follows.Section 2 provides literature review. Section 3 describes data col-lection and research methodology. Section 4 presents the findings.Section 5 provides conclusions.
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2. Literature review
2.1. National innovation system
The high-tech industry plays a vital role in a country’s globalcompetitiveness. Numerous studies have examined determinants
Please cite this article in press as: W.-M. Lu et al., Intellectual capital and natdx.doi.org/10.1016/j.knosys.2014.08.001
of industrial economy and global competitiveness (for example,[7,27,34,51,63,80]. Although there is no single accepted definitionof NIS, the importance of NIS has been emphasized in variousresearch such as business and industry [5,11,15,38,43,79], science[40], and policy-making institutions [1,19,22,42,46,58].
The institutional interactions related to innovation and theunderlying production systems are the basic characteristics ofNIS. The basic meaning of NIS is the national innovation effortinvested by each division, which is transformed into economicdevelopment and productivity growth that ultimately gear towardnational competitiveness. In summary, the definition of NIS in thisstudy encompasses the developmental concept of industry, gov-ernment, and academia to investigate the network operating rela-tionship among business, academia, and research systems. Fromthe innovation and knowledge creation perspective, NIS createstechnological innovation, stimulates the development of thenational economy [26,28,35,44,57], and ultimately the nationalcompetitiveness of a country [76].
OECD [60] analyzes the efficiency and interaction of variousinnovation elements with knowledge creation, proliferation, andapplication through a series of indicators. McKelvey [53] suggeststhat the adoption of NIS in new technology is dissimilar. WhileFreeman [28] emphasizes improvement on social and policy-related issues, Lundvall et al. [49] focuses on the interactive learn-ing between the manufacturer and the customer. Nelson [57]emphasizes the company capacity and the creation of innovativeroutine. Freeman [28] points out that the interaction and mutualfeedback among NIS members including enterprise, academia,and research institutions are critical to NIS performance. Specifi-cally, R&D policy is an important factor that affects national pro-ductivity. However, changes in national productivity cannot fullyexplain differences in R&D policy, which is still likely to distortmarket forces. Nasierowski and Arcelus [56] are the first scholarsto apply non-parametric DEA to unlock the relationship betweencountries’ commitments and both technological efficiency and pro-ductivity, in order to measure the input and output conversion pro-cess of NIS. The findings show that the interaction variable affectsthe overall efficiency.
2.2. Intellectual capital
The term ‘‘IC’’ was first proposed by the economist Galbraith[31]. He states that IC refers to the behavior of using brain insteadof just using knowledge and mere intelligence. In order to createvaluable IC, an organization should establish valuable organiza-tional network to link the interdepartmental working team, andalso to link the external parties like customers and suppliers toaccelerate the company’s value creation. Skandia led the way in1994 by developing and issuing the first IC report in addition totraditional financial report in order to convey supplementary infor-mation on its effort in measuring knowledge assets. Based on Skan-dia work, Edvinsson and Malone [24] categorize IC into humancapital and structural capital. The structural capital is further sub-divided into customer capital and organizational capital. They alsoprovide market structure and guidelines of IC. However, there is noclear and consistent definition of IC. In summary, IC can be definedas intangible assets, or the value whereby market value exceedsbook value, which in turn will enhance organizational value andnurture firms’ competitive advantage [25].
McElroy [52] proposes another theory, which highlights thatextant IC measurement neglects social capital. According to thesocial capital theory, the mutual trust, mutual benefit, commonvalues, networks, and social norms that exist in an organizationitself and other organizations can increase the organizational val-ues and inter-organizational values, which can speed up the trans-fer of information and the development of new knowledge. The
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theoretical social innovation capital is the firms’ collective capacityof innovation, which can be considered as the most valuable typeof IC that forms the basis of organizational learning, innovationand adaptation ability. Thus, he proposes to incorporate social cap-ital into Skandia Market Chart and suggests that IC should com-prise human capital, social capital, and structural capital.
This study summarizes the classification of IC done by priorscholars and finds that the definitions of IC differ according toorganizational culture and environment. Note that the focus ofour study is to assess the NIS efficiency at national level and to con-duct cross-countries comparison. Thus, we consider the impact ofthe characteristics of social culture and innovation. IC is dividedinto human capital, social capital, and innovation capital[21,52,56,75]. The relevant proxy measures of IC are summarizedin Table 1.
There are some other IC-related studies. For example, Lynn [51]investigates the cultural factors of different countries and the crit-ical success factors of IC management using six companies fromthe United States, Canada, and Sweden as case studies. Theresearcher adopts Hofstede [36] four cultural dimensions indica-tors, which comprises power distance, individualism, masculinity,and uncertainty avoidance, to compare the differential impacts oforganizational culture and national culture on IC management.The findings show that culture plays an important role in IC man-agement. Inkeles [39] claims that the indicators in Hofstede [36]fall under social capital.
Dakhli and De Clercq [21] analyze the comprehensive develop-ment of a country to test the relationship between human capitaland innovation. The researchers employ the conceptual social cap-ital trust, organizational activities, and the citizens’ code of conductto test the relationship between social capital and innovation indi-cators. Based on different social dimensions of 59 countries, theirempirical results offer a theory-based model in that innovationinvolves human capital and social capital. The value creation ofthese two forms of capital provides a global point of view. Thereis a positive relationship between human capital and innovation,as well as between human capital and the social capital trust,and innovation in organizational activities. Summing up, humancapital, innovation capital, and social capital are important intangi-ble assets that drive social network, create economic value, anddevelop national competitiveness.
3. Research design
3.1. Network production process for NIS
Evaluating cross-countries NIS performance is a complex pro-cess that needs to take into account multiple indicators. Employingmultiple inputs and multiple outputs, this study constructs a net-work production model [17,41,47,50,68,69,81] that comprisesR&D efficiency and economic efficiency to capture the linkages ofinternal economic activities effectively. Through evaluation of the
Table 1Intellectual capital indicators.
Intellectualcapital
Indicators (proxy variables)
Human capital The number of employees, percentage of managers who have highemployees, the average professional salary, annual staff training tim[9,12,24,66,72]
Social capital Universal trust, organizational trust, community activities, citizensindividualism, masculinity, uncertainty avoidance [36,55,56]
Innovationcapital
The ratio of R&D expenses to total revenue, the ratio of R&D resoumaintenance costs, effective use of the number of patents, incomeof R&D personnel [23,24,66,72,75]
Please cite this article in press as: W.-M. Lu et al., Intellectual capital and natdx.doi.org/10.1016/j.knosys.2014.08.001
cross-countries NIS efficiency, we identify countries that are rela-tively competitive and efficient as benchmarks for other countries.The inputs, outputs, and the network production process model areshown in Fig. 1.
3.1.1. R&D efficiency stageInput variables: Labor and capital are inputs that will directly
affect the current and future development of NIS. The human com-ponents include total R&D personnel nationwide (RDP) and totalpublic expenditures on education (EDU), while the capital compo-nents include the import of goods and commercial services (ICGS)and the total expenditures on R&D (EXRD). All of these input fac-tors are deemed important investments in NIS.
Human resources are important assets of a country’s economicand technological development. RDP such as well-trained andskilled engineers, the willingness to work hard and astutely, theprofessional skill and management style involved, and integrativecapabilities to make an energetic and risk-taking entrepreneurialfirm are the main indicators to evaluate basic R&D ability[56,62,77]. In terms of enhancement of R&D ability, EDU can trainand recruit talents in innovation to improve knowledge innovationand academic excellence. These initiatives can promote knowledgeaccumulation and create first-class research university to supportR&D. In this knowledge-based era, continuous innovation in tech-nological education will not only help industry upgrade and growin a sustainable way, but also serve as a key factor to enhancenational competitiveness [37,56,70].
In developed countries, the service industry evolves and gradu-ally replaces the traditional industries. With values of the innova-tive service industry increasing rapidly, the developed countrieshave become prominent players in the NIS global competitionthrough development in knowledge-intensive industries. ICGS isan important factor in fostering a country’s NIS [56,62], whileEXRD is a crucial input that can affect patents, intellectual prop-erty, and academic research. [37,56,62].
Output variables: The output variables of technological researchrepresent the skillfulness of a country in terms of investment invarious R&D aspects. There are three types of output that are gen-erally deemed as the final output in the R&D production process.First, published scientific articles (SAP) are concrete outputs oftechnological and academic research. SAP also serves as theoreticalbasis for R&D and strengthens a country’s development of techno-logical education. Therefore, this study uses journal articlesindexed in Science Citation Index, Social Sciences Citation Index,and Engineering Index as the proxy [56,62,77]. Second, whenR&D resources are constantly invested in large scale, the numberof patents received is the most representative output indicator.For any economic entity in any country, patents not only protectthe inventor of intellectual property but also show its unique andexclusive position in the market. Patents contribute significantlyto NIS internal network R&D diffusion behavior and the acquisitionof economic benefits. Thus, this study employs patents granted to
academic qualifications, employee turnover, average salary, the average age ofe, annual staff training costs, the scale of the project team, employee satisfaction
hip behavior norms [21]; Hofstede’s four cultural dimensions: power distance,
rces to total resources, patents, copyrights, trademarks, the amount of patentobtained via patent, the ratio of revenue to management expense, the proportion
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Fig. 1. Network production process model of the national innovation system.
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residents (PAGR) as another output proxy in measuring R&D effi-ciency [37,56,67,77]. Third, patents secured abroad by country res-idents (PSA) should be utilized as a business tool in the globalmarket to ensure that a leading position can be secured in theintense competitive global business environment. Thus, to enhancethe rigorousness of our study, we employ effective PSA as outputvariable for R&D efficiency [37,56,58,62,77].
In summary, this first stage examines whether the ICGS andEDU improve the knowledge absorption capacity of citizens basedon the concept of R&D. Furthermore, through RDPN and EXRD, out-puts can be maximized, while input can be minimized for betterperformance. As a result, we name this stage as R&D efficiency.The input variables include ICGS, EXRD, RDPN, and EDU, whereasthe output variables include SAP, PAGR, and PSA.
3.1.2. Economic efficiency stageInput variables: SAP, PAGR, and PSA, the output from R&D effi-
ciency stage, are inputs for economic efficiency stage. The rationaleis that NIS is formed by the interactions and functions of innova-tion generation, diffusion, and application of various organizations.When there are proper interactions among these institutions, NISwill function effectively and can become a driving force for eco-nomic growth. The idea of the network model is that R&D capabil-ity should enhance national economic growth. Therefore, thisstudy treats the outputs of R&D efficiency stage, i.e. SAP, PAGR,and PSA as the connecting factors or intermediate variables fornational internal economic activities.
Output variables: Gross Domestic Product (GDP) is a measure ofa country’s overall territorial economic output. It is the marketvalue of all final goods and services officially made within a coun-try in a year. Gross National Product (GNP), on the other hand, isthe market value of all goods and services produced in one yearby labor and property supplied by the residents of a country. Itreflects the average income of a nation’s citizens. GDP and GNPare different. In its simplest terms, GDP is the value of goods andservices made in a country, while GNP is the value of goods andservices made by residents. As the world becomes connected, itis common that nations and industries have a multinationalhuman resources allocation. Since we evaluate national decision-making units (DMUs), i.e. the national innovation systems of 30countries in this study, we use GDP as an output proxy in measur-ing economic efficiency [55,56].
When comparing GDPs of different countries, the domestic cur-rency has to be converted. There are two types of conversion, thefirst is using the international exchange rate, and the second isusing purchasing power parity (PPP). The PPP is a concept thattwo countries’ exchange rates of currency equalize the differencein purchasing power between the two currencies. If the countries’GDP is obtained through the above two methods, the ranking willdiffer. If we use the international exchange rate, it is unfair to those
Please cite this article in press as: W.-M. Lu et al., Intellectual capital and natdx.doi.org/10.1016/j.knosys.2014.08.001
developing countries as the domestic purchasing power of con-sumers and producers could be underestimated. Therefore, weemploy PPP as another output proxy in measuring economic effi-ciency [55,56].
Productivity is a measure of output from a production process,per unit of input. The definition of productivity is using resourcecreatively and effectively, and increasing the values of productsand services. Productivity growth depends mainly on a country’snatural resources and accumulation of human capital. It enhancesthe technical level and improves the institutional environment. Asa result, productivity growth determines the expansion of variousresources, and affects the economic growth of a country [55,56].Productivity (PROD), measured as the difference between real out-put value and real input value, serves as another output variable.Summing up, this second stage investigates the economic out-comes of investment in SAP, PAGR, and PSA. The outputs areGDP, PPP, and PROD.
3.2. Data collection and descriptive statistics
In this study, all of the inputs and outputs data are extractedfrom National Science Indicators on Diskette, ISI Co., USA, WorldCompetitive Yearbook (WCY) produced by International Instituteof Management Development (IMD), and proxy statements. In con-ducting the performance measurement in this study, we employthe average values of variables for different countries over the per-iod 2007–2009, which have been adjusted properly by IMD. Therequirement to establish an international comparison imposes cer-tain restrictions in obtaining a transnational homogeneous sampleof national innovation system in terms of specialization. Anotherconcern is the laggedness of outcomes relative to certain inputs.For example, the effect of SAP on final outputs is not immediate.As noted above, we use average values over the sample period,which solve the problem of laggedness [30]. After data screening,we have 30 countries as sample.
According to Cooper et al. [20], the minimum number of DMUobservations should be three times greater than the number ofinputs and outputs used when using the DEA model. In this study,the number of DMU is 30, which is at least three times greater thanthe selected variables in each stage. The two models proposed inthis study conform to this requirement. Descriptive statistics ofthe inputs and outputs are provided in Table 2.
Descriptive statistics show only the distribution of variables,but not the isotonicity relationship between the input and outputvariables. As a result, we follow Golany and Roll [32] to analyzethe correlation between inputs and outputs. The results of correla-tion coefficients are shown in Tables 3 and 4. All of the coefficientsare positive. We hence conclude that the developed network DEAmodel of the R&D efficiency stage and economic efficiency stageholds high construct validity.
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Table 3Correlation coefficients between inputs and outputs in the first stage (R&D efficiency).
Input variables Output variables
SAP PASA PAGR
PUR 0.868 0.388 0.457GERD 0.699 0.784 0.902REM 0.919 0.547 0.475EDU 0.787 0.436 0.585
Table 4Correlation coefficients between inputs and outputs in the second stage (economicefficiency).
Input variables Output variables
GDP PPP PROD
SAP 0.898 0.927 0.858PASA 0.604 0.468 0.585PAGR 0.682 0.392 0.682
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3.3. Slack-based measure in network DEA
The Tone and Tsutsui [73] network DEA model is employed inthis study. The model is based on the weighted slack-based mea-sure (SBM) approach that seeks optimal solution and accountsfor the importance of each stage. Solving the black-box issue, weare able to consider the link within an entity. Moreover, we canevaluate each stage of DMUs’ performance [48,54].
The traditional DEA models (such as CCR and BCC models) thatutilize the radial measure of efficiency are not appropriate in somecases because some of the inputs or outputs are substitution anddo not change proportionally. Tone and Tsutsui [73] propose a net-work SBM to measure organizational efficiency. The SBM is a non-radial method and is suitable for measuring efficiencies wheninputs and outputs may change non-proportionally. The SBM canbe divided into three kinds: input-oriented SBM, output-orientedSBM, and non-directional SBM.
Accounting for both input and output slacks, the non-orientedefficiency for the observed DMU (o = 1, . . . , n) can be formulatedby the following fractional programming problem:
TEo ¼ MIN1K
XK
k¼1
1� 1mk
Xmk
i¼1
sk�
i
xkio
!" #,1þ 1
pk
Xpk
r¼1
skþr
ykro
!" #
s:t:
xkio ¼
Xn
j¼1
xkijk
kj þ sk�
i ; i ¼ 1; . . . ;mk; k ¼ 1; . . . ;K;
ykro ¼
Xn
j¼1
ykrjk
kj � skþ
r ; r ¼ 1; . . . ; pk; k ¼ 1; . . . ;K;
Xn
j¼1
kkj ¼ 1; k ¼ 1; . . . ;K;
Xn
j¼1
zðk;hÞtj kkj ¼
Xn
j¼1
zðk;hÞtj khj ; t ¼ 1; . . . ; sðk;hÞ;8ðk; hÞ;
kkj P 0; kh
j P 0; sk�
i P 0; skþ
r P 0;8k;
ð1Þ
where xkij is the amount of input i to the DMU j at stage k; yk
rj is the
amount of output r to the DMU j at stage k; zðk;hÞtj is the amount of link-ing intermediate product t from stage k to stage h to the DMU j; n isthe number of DMUs, K is the number of stages (k = 1, . . . , K); 1/Krepresents is equal weight at each stage; mk represents the numberof inputs at stage k; pk represents the number of outputs at stagek; s(k,h) represents the number of items in link from stage k to stageh. The kk
j represents the weight an observed DMU references from
DMU j so as to achieve target efficiency at stage k. sk�i and skþ
r arethe slack values at stage k for inputs and outputs, respectively.
Table 2Descriptive statistics of the input and output variables.
Variables Mean Minimum Maximum Standard deviation
Input variablesPUR 314,904 1,173,167 36,466 273,764GERD 17,586 148,610 472 29,766REM 181,302 1,534,267 15,833 310,448EDU 38,097 142,375 4,230 41,478
Intermediate variablesSAP 48,181 246,180 6,222 53,695PASA 10,288 127,644 65 26,801PAGR 5,537 71,455 22 13,383
Output variablesGDP 953,425 4,549,800 118,400 1,128,516PPP 1,076,189 8,093,000 110,000 1,584,774PROD 1,988,285 9,093,405 229,652 2,269,625
Please cite this article in press as: W.-M. Lu et al., Intellectual capital and natdx.doi.org/10.1016/j.knosys.2014.08.001
This fractional program problem can be solved by being trans-formed into a linear program using the Charnes and Cooper trans-formation [14]. Let an optimal solution to Eq. (1) be (kk�
j ; s��i ; sþ�r ) for
the observed DMU. If TE�o ¼ 1 in Eq. (1), the observed DMU is calledoverall efficient. Meanwhile, a value of less than 1 on TE�o meansthat the observed DMU is overall inefficient.
The stage k efficiency score for the observed DMU can bedefined by
TEk�o ¼ 1� 1
mk
Xmk
i¼1
sk��i
xkio
!" #,1þ 1
pk
Xpk
r¼1
skþ�r
ykro
!" #; k ¼ 1; . . . ;K:
ð2ÞThe overall efficiency score is the equal weighted mean of the K
stage scores:
TE�o ¼1K
XK
k¼1
TEk�o ð3Þ
472
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3.4. Truncated regression
Prior studies have applied Tobit regression to examine theimpacts of exogenous factors on DEA scores since efficiency valuesobtained range between 0 and 1. [8,18,64,65,74,78]. However,Simar and Wilson [71] verified that truncated regression was moreappropriate than Tobit regression. In other words, they illustratedthat Tobit regression was inappropriate to analyze the efficiencyscore under DEA. Their results indicate that truncated regressionis superior to the Tobit regression. Therefore, following Simarand Wilson [71], we use truncated regression model to investigatethe association between intellectual capital and the performance ofNIS. The model is as follows:
hj ¼ aþ Zjbþ ej; j ¼ 1; . . . ;n ð4Þ
In Eq. (4), the scalar a and the (d � 1) vector b are unknown parame-ters to be estimated; Zj is a (1 � d) vector of observation-specific vari-ables (intellectual capital) for NIS that we expect it to be related to theNIS overall efficiency score, which is proxied by hj. Since the distribu-tion of ej is restricted by the condition ej P 1 � a � Zjb, Eq. (4) is mod-ified to get Eq. (5), which assumes that the distribution beforetruncation is truncated with zero mean, unknown variance and atruncation point, which are determined by different conditions:
hj � aþ Zjbþ ej; j ¼ 1; . . . ;n
where
ej : Nð0;r2e Þ; such that ej P 1� a� Zjd; j ¼ 1; . . . ;n:
ð5Þ
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Table 5National innovative system performance value.
DMU R&Defficiency
Economicefficiency
Overallefficiency
Area
Australia 1.000 0.513 0.757 OceaniaNew Zealand 1.000 0.175 0.587 Oceania
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The regression process of parametric bootstrapping is used to con-struct the bootstrap confidence intervals for the estimates ofparameters ðd;r2
e Þ, and to estimate Eq. (5) by maximizing the corre-sponding likelihood function, and give heed to the ðd;r2
e Þ. For thesake of brevity, the reader can refer Simar and Wilson [71] for thedetails of the estimation algorithm.
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Brazil 1.000 1.000 1.000 Latin AmericaMexico 0.391 1.000 0.695 Latin AmericaAustria 0.548 0.306 0.427 EuropeBelgium 0.547 0.377 0.462 EuropeCzech Republic 1.000 0.237 0.618 EuropeDenmark 1.000 1.000 1.000 EuropeFinland 1.000 0.565 0.783 EuropeFrance 0.623 1.000 0.812 EuropeGermany 1.000 1.000 1.000 EuropeGreece 1.000 0.549 0.774 EuropeIreland 1.000 0.269 0.635 EuropeNetherlands 1.000 0.601 0.800 EuropeNorway 0.782 0.462 0.622 EuropePoland 1.000 1.000 1.000 EuropePortugal 1.000 0.434 0.717 EuropeSpain 1.000 1.000 1.000 EuropeSweden 0.647 0.412 0.529 EuropeSwitzerland 1.000 1.000 1.000 EuropeBritain 1.000 1.000 1.000 EuropeChina 1.000 1.000 1.000 AsiaHong Kong 1.000 1.000 1.000 AsiaJapan 1.000 1.000 1.000 AsiaKorea 1.000 1.000 1.000 AsiaMalaysia 1.000 1.000 1.000 AsiaSingapore 1.000 0.137 0.568 AsiaTaiwan 1.000 1.000 1.000 AsiaThailand 1.000 1.000 1.000 AsiaTurkey 1.000 1.000 1.000 AsiaAverage 0.918 0.735 0.827
Note: Overall Efficiency = 1/2 (R&D Efficiency + Economic Efficiency).
4. Empirical analysis
4.1. National innovation system efficiency analysis
Table 5 shows the results of the network DEA model. The meanvalues of R&D efficiency, economic efficiency, and the overall effi-ciency are 0.918, 0.735, and 0.827. The results show that the R&Dperformance is better than the economic performance, indicatingthat most countries have good R&D capabilities, but have failedto create economic value through their R&D capabilities.
In terms of the R&D efficiency stage, 24 countries are deemedefficient, accounting for 80% of the sample. Among the regions,the average R&D performance of countries in Oceania and Asia arethe best, reaching the value of 1. Meanwhile, countries in The Amer-icas have the lowest value of R&D efficiency (0.696). With respect tothe economic efficiency stage, 17 countries (approximately 57%) arefound to be efficient. The regional average performance of theAmerican national innovation system is the best (1.000), followedby Asia (0.940), Oceania (0.735), and Europe (0.659).
To understand whether significant differences on the efficiencyperformance exist among the different regions, this study uses theKruskal–Wallis test [10]. At the significance level of 5%, we showthat there is no significant difference on the performance of R&Defficiency stage among the regions. However, there is a significantdifference on the performance of economic efficiency stage amongthe regions. Details are shown in Table 6.
4.2. Intellectual capital and the national innovation systemperformance
IC sources of future benefits that lack physical embodiment canbe regarded as a contributor to national economic growth. A coun-try’s NIS aims at increasing the R&D and economic value. Having awell-developed system will be beneficial to all residents; therefore,enhancing national competitiveness so as to grow will be the pri-ority of governments. The best and perhaps only way for countriesto perform effectively and efficiently is through a strong combina-tion of IC and the development of NIS. To explore the relationshipbetween intellectual capital and the NIS performance, we use threevariables that are related to the dimensions of human capital, inno-vation capital, and social capital. They are described as follows,respectively. The IC-related data are extracted from the Greet Hof-stede Analysis2 database.
4.2.1. Human capitalThe number of employee is the substantial index of human cap-
ital [24,45]. Employees who have R&D talent play a core role in NISand they contribute to productivity growth and competitivenessby utilizing tangible assets and intangible assets in an organization.From the perspective of NIS, the proportion of R&D talents willaffect the innovation activities. Therefore, this study employs theratio of the number of R&D talents to working population as aproxy of human capital. This variable reflects the distribution ofinnovative human resources, and reveals the competitivenessand innovativeness of this country.
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2 Greet Hofstede Analysis database are constructed by Geert Hofstede et al. thewebsite: http://www.geert-hofstede.com/hofstede_dimensions.php.
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4.2.2. Innovation capitalVan Buren [75] provide a succinct definition of innovation cap-
ital in an organization. That is, three core proxies used to measurethe performance are the expenditure of R&D, the percentage ofinnovation in works, and products’ innovativeness. In essence, theyare used in the production process of NIS to provide a country witha competitive edge. Furthermore, all of the inputs in R&D expendi-ture, the number of R&D labors, the cost of training and education,and the import and export business could bring about ‘‘patent’’ asthe major output. Accordingly, we employ patent productivity asthe proxy of innovation capital. Successful innovation capital isin fact a reflection of the contribution by R&D employees. More-over, it also reveals whether the difference in the degree of innova-tion capital will affect the efficiency of the whole system.
4.2.3. Social capitalAccording to Inkeles [39], the ‘‘uncertainty avoidance’’ variable
that is developed as a proxy to measure cross-cultural differencesby Hofstede [36] can be classified as a part of social capital. In addi-tion, Nasierowski and Arcelus [56] also have confirmed that‘‘uncertainty avoidance’’ variable has played an intermediary rolein the NIS performance. Uncertainty avoidance is the level of riskaccepted by a culture of a nation, which can be gleaned from theemphasis on rule adherence, ritual behavior, and labor mobility.Culture of a nation with low levels of uncertainty avoidance hasgreater tolerance for ambiguity and a less need for formal rules.The opposite will hold true for high-uncertainty avoidance cul-tures. On the other hand, if a nation has a high level of uncertaintyavoidance, she will have more rigorous planning in the process ofinnovation system in order to avoid the occurrence of uncertain-ties. With rigorous planning, a nation can reduce the cost of non-essential spending on those uncertainties and the economics ofthe nation will be more stable and ultimately prospers. As a result,‘‘uncertainty avoidance’’ is utilized as the proxy of social capital.
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Table 7Descriptive statistics of intellectual capital.
Variable Mean Standard deviation Minimum Maximum
HC 0.006 0.003 0.001 0.013InC 5.653 10.629 0.270 52.030SC 62.226 26.080 8.000 112.000
Note: HC and InC are measured in percentage, while SC is measured as an index.
Table 6The average performance of the network model by region.
Area Number of countries R&D efficiency mean Kruskal–wallis test (p-Value) Economic efficiency Mean Kruskal–Wallis test (p-Value)
Oceania 2 1 0.142 0.344 0.036*
Latin America 2 0.696 1Europe 17 0.891 0.659Asia 9 1 0.940Overall 30 0.918 0.735
* p-Value < 0.05.
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This paper assumes and tests the regression model as follows:
TEj ¼ aþ b1HCj þ b2InCj þ b3SCj þ ej; j ¼ 1; . . . ;n ð6Þ
Using the truncated regression [71], we investigate whether IC hassignificant effects on the NIS performance. TEj is the DEA score. HCj
is the human capital; InCj is the intellectual capital; RCj is the socialcapital.
Table 7 presents the descriptive statistics of the IC variables,while the empirical results analysis is shown in Table 8. All ofthe independent variables are significantly related to the NISperformance.
In summary, in order to enhance the overall performance ofnational innovation systems, we suggest that governments recruithigh-quality R&D team to strengthen global competitiveness. Withrespect to innovation capital, governments should increase theopportunities of innovation and research exchanges, as well asenhance the quality and quantity of outcome of the patent andother intellectual property to expand its economic benefits. Wealso suggest that governments could enhance the social capitalby actively linking government agencies, business organizations,civil society, and academic research agencies to enhance nationalsynergy effect.
4.3. Discussion
4.3.1. Decision-making matrixThis study further explores the condition of R&D efficiency and
economic efficiency in NIS. The correlation between the two stagesis significantly positive (coefficient = 0.105). We construct a mana-gerial matrix for decision making. In Fig. 2, the horizontal axis rep-resents R&D efficiency and the vertical axis represents economic
Table 8The results of truncated regression models.
Variables Coefficient
Intercept –2.679HC (the number of R&D talents to working population) 4.360InC (patent productivity) 0.372SC (uncertainty avoidance) 1.123R2 0.224
*** p-Value < 0.01.** p-Value < 0.05.
* p-Value < 0.10.
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efficiency. By assigning each DMU to one of the four zones, wecan clearly define the benchmark to be emulated by inefficientcountries. This provides an insight for governments to developappropriate strategies for enhancing their overall efficiency. Theanalysis result as follows.
Zone A: The countries in this zone exhibit better R&D efficiencyand economic efficiency than those in other zones. There are fif-teen countries in this zone: Brazil, Denmark, Germany, Poland,Britain, Switzerland, Malaysia, Turkey, China, Spain, Hong Kong,Taiwan, Thailand, Japan, and South Korea. They can be deemedas a role model for inefficient countries.
Zone B: This zone includes two countries, namely Mexico andFrance, which have lower R&D efficiency but relatively efficienteconomic performance. We suggest that these two countriesshould strengthen the foundation of energy and enhance industrialcooperation, and continue to interact with various internationalinstitutions in order to reduce a great waste of R&D resources.Remodeling and renewing the process of R&D will increase thebenefits to reach the synergy effect. Results from the uncertaintyabout the outcome of R&D suggest that technology is the prioritytask that would enable accurate and fast collection of information,and would protect the outcome of R&D patent and the intellectualproperty rights. In the aspect of human resource, they should hirehigh-quality technology professionals, and put emphasis on train-ing and education to improve the ability of R&D management.
Zone C: This zone contains nine countries, namely Australia,New Zealand, the Czech Republic, Finland, the Netherlands,Ireland, Greece, Portugal, and Singapore. Their R&D performanceis efficient but their economic efficiency is substandard. Theyshould focus on reducing operating costs and makingmarket-demand-oriented managerial strategies decision. Theycan select the key success factors for the development strategy,and commit to operating diversification in order to increase thevalue added and to maximize the economic value. In this case,technology that mainly lies in the innovation process are market-ing communication and channel expansion, which are meant forprofiting from R&D diffusion. As for the human resource, the gov-ernments should strengthen corporation among the industries,government, and academic institutions to increase the diffusionof R&D outcome to improve the overall efficiency of the nations.
Std. error Z-stat p-Value
1.924 –1.392 0.0812.117 2.059 0.019**
0.128 2.899 0.002***
7.767 1.464 0.072*
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Zone D: The four countries in this zone are Austria, Belgium,Sweden, and Norway. They are inefficient in R&D and economicperformances. We suggest that these four countries should fullyapply the improvement strategies in other three zones. For exam-ple, they should emphasize the strength of the R&D performance;managers should get rid of the concept that R&D creates greatercosts and accentuate the concept of unit efficiency. To increasethe economic efficiency, they should integrate the enterprise valuechain, and participate in industry cooperation and technologytransformation to strengthen the competitiveness of their NIS.
Fig. 3. Performance impro
Please cite this article in press as: W.-M. Lu et al., Intellectual capital and natdx.doi.org/10.1016/j.knosys.2014.08.001
4.3.2. Performance improvement strategy mapAs Section 2 points out, IC plays a key role in improving the NIS
performance. In this subsection, we discuss a performanceimprovement strategy map discussed. Continuing from decision-making matrix, we provide suggestions for countries in each quad-rant to improve, focus, and strengthen their NIS through IC. Fig. 3shows the map.
The performance improvement strategy for countries in quad-rant A: We recommend the countries (i) to continue strengtheninginternational exchanges, cooperation in science and technologypolicy projects, international intellectual property rights, and eco-nomic diffusion capacity (technology-related policy), (ii) tocontinue focusing on international trade and investment agree-ments, as well as regional innovation and the effectiveness of thenational innovation projects (industry-related policy), (iii) toachieve economic growth and to integrate innovation with envi-ronmental issues, in order to adapt to the fast-changing worldtrends (innovation-related policy).
For example, Japan is far ahead of other countries in innovatingnew technology because Japan depends not only on the scale ofR&D, but also the close linkage with the social and institutionalchange. The Department of the Government of Japan promoteseconomic modernization, with the Ministry of Economy, Tradeand Industry (METI) playing the role of to promote high-techdevelopment, long-term development of the world market for itsmain functions, and close cooperation between large domesticindustry and the Government (human capital strategy). The enter-prise-level R&D strategy and technology introduction and theinnovative changes viz. ‘‘factory is laboratory’’ bring about changesin innovation (innovation capital strategy). Training and education
vement strategy map.
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in science and technology have made the concept of quality andinnovation common in Japan, thus resulting in an environmentwhere Japanese pay attention to the uniqueness and innovative-ness of goods and services in Japan (social capital strategy), andultimately the Kingdom of innovation is formed [29]. Takentogether, Japan can serve as the benchmark for countries in otherquadrants to improve.
The performance improvement strategy for countries in quad-rant B: From the intellectual capital viewpoint, we recommendthat the countries should train and reward scientific and techno-logical personnel (human capital strategy). Note that countries inthis quadrant are inefficient in the R&D efficiency stage. Therefore,the countries should promote industry-university cooperation toenhance research and development. For example, they canimprove innovative capital structure by integrating innovationlearning into industry clusters (innovation capital strategy). More-over, through production technology and innovative strategic alli-ances, innovation infrastructure can be improved and theallocation of resources can be optimized. To enhance learning effi-ciency, cooperation between university and industry is necessary.
The performance improvement strategy for countries in quad-rant C: We recommend that countries in this quadrant to establisha better education and training system. Specifically, they shouldimprove applied science and technology project managementand innovative service quality (human capital strategy). The coun-tries should advocate strategic planning on technology commer-cialization, product commercialization, and technology transfer.They should also encourage entrepreneurial investment (innova-tion capital strategy). Organizational process re-engineering andre-engineering, innovation-active corporate culture and goodwill,and e-commerce function are all important tactics to shorten leadtime (social capital strategy).
The performance improvement strategy for countries in quad-rant D: We recommend countries in this quadrant to enhancethe research and development capability as well as the educationand training system. They should create supply and demand mar-kets. To achieve that, they may have to tighten their defenseresources and cooperate with others (human capital strategy).Engaging in strategic alliances, encouraging studies in innovation,integrating technology diffusion among industry clusters areamong the potential approaches for the countries to improve theirR&D and economic efficiencies (innovation capital strategy). Thegovernment should give tax incentives or incentive allowances.Institutional bodies also should support and promote innovation.Financial system support should be built (social capital strategy).
803804805806807808809810811812813814815816817818819820821822823824825826827828829
5. Conclusion
This study employs a network DEA model to investigate the effi-ciency of NIS. From the findings, we offer some managerial impli-cations through a decision-making matrix. Moreover, wedetermine the impact of intellectual capital on the overall perfor-mance using truncated regression. Our research findings may offersome suggestion to improve the NIS performance and resourceallocation. The findings are as follows. Generally, NIS is efficientin R&D context but needs improvement in economic efficiency.America NIS is the most efficient one in terms of both R&D effi-ciency and economic efficiency stages. The countries that areefficient in R&D efficiency stage are not necessary efficient in theeconomic efficiency stage. The countries that are efficient in bothstages include Brazil, Denmark, Germany, Poland, the United King-dom, Switzerland, Malaysia, Turkey, China, Spain, Hong Kong, Tai-wan, Thailand, Japan, and South Korea. The overall efficiency ofR&D inefficient countries can be improved through economic effi-ciency. For example, Mexico and France are inefficient in R&D
Please cite this article in press as: W.-M. Lu et al., Intellectual capital and natdx.doi.org/10.1016/j.knosys.2014.08.001
efficiency but their overall efficiencies are still satisfactory becauseof good efficiencies in economic performance. With regards to ourregression analysis, IC is significantly related to NIS efficiency. Thisresult shows that the recruit of high quality R&D talent, participa-tion in industry, government, academia, research institutions toincrease innovation and research exchange opportunities couldimprove the quality and quantity of R&D outcomes. Educationaltraining can be held frequently to strengthen organizational com-petitiveness and foster teamwork to further maximize the organi-zational economic benefits.
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