XXII. MEZINÁRODNÍ KOLOKVIUM O REGIONÁLNÍCH
VĚDÁCH. SBORNÍK PŘÍSPĚVKŮ
22nd
INTERNATIONAL COLLOQUIUM ON REGIONAL
SCIENCES.CONFERENCE PROCEEDINGS
Place: Velké Bílovice (Czech Republic)
June 12-16, 2019
Publisher: Masarykova univerzita (Masaryk University Press), Brno
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Viktorie KLÍMOVÁ
Vladimír ŽÍTEK
(Masarykova univerzita / Masaryk University, Czech Republic)
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XXII. mezinárodní kolokvium o regionálních vědách Sborník příspěvků Velké Bílovice 12.–14. 6. 2019
 
177
DOI: 10.5817/CZ.MUNI.P210-9268-2019-22
EFFICIENCY OF INTELLECTUAL CAPITAL GENERATION:
A DEA ANALYSIS OF SELECTED EU REGIONS
Efektivnost tvorby intelektuálního kapitálu: DEA analýza vybraných
regionů EU
HENRY JUNIOR ANDERSON
SOLOMON GYAMFI
JAN STEJSKAL
Ústav ekonomických věd
Fakulta ekonomicko-správní
Univerzita Pardubice
Institute of Economic Sciences
Faculty of Economics and Administration
University of Pardubice
 Studentská 95, 532 10 Pardubice, Czech Republic
E-mail: henry.anderson@upce.cz, solomon.gyamfi@upce.cz, jan.stejskal@upce.cz
Annotation
Intellectual capital generation has gradually been entrenched as a measure of firm and regional competitive
advantage in this era of knowledge economy. Firms, regions and countries and even international organisations
around the world have rigorously engaged in the creation of property rights for control and usage. However, as
regional innovative performance has been directly connected with intellectual capital creation, we question
whether regions with high innovation prowess efficiently employ the scarce financial and cognitive resources that
are harnessed for the manifestation of intellectual capital and property rights. Hence, using a multi-stage DEA
approach and data of ten countries from European Innovation Scoreboard, we aim to comparatively compute the
efficiency scores of innovation leaders and strong innovators to reveal the efficiency of resource inputs. Results at
the first-stage revealed a mixed efficiency performance amongst countries whilst second-stage analysis with
intermediate inputs revealed a dominance of innovation leaders on the efficiency ranking but eventually innovative
leaders were found to be generally efficient in financial and cognitive resources employed in intellectual capital
generation. We further gave recommendations on way of bolstering efficiency scores of countries.
Key words
efficiency, intellectual capital, DEA analysis
Anotace
Tvorba duševního kapitálu se v období vzrůstu zájmu o znalostní ekonomiku postupně etablovala jako měřítko
regionální konkurenční výhody. Firmy, regiony, země a dokonce i mezinárodní organizace po celém světě se
důsledně zabývají tvorbou vlastnických práv k jejich komerčnímu využití. Vzhledem k tomu, že tvorba inovací v
regionech je přímo spojena s tvorbou intelektuálního kapitálu, je nezbytné diskutovat, zda regiony s vysokou
inovační schopností efektivně využívají vzácných finančních a znalostních zdrojů, které jsou využívány k tvorbě
duševního kapitálu a následnému komerčnímu využití vlastnických práv. Studie využívá vícestupňovou DEA
analýzu a na datech European Innovation Scoreboard z desíti vybraných zemí EU analyzuje efektivnosti
znalostních procesů v jednotlivých regionech. Výsledky v první fázi ukázaly proměnlivou efektivnost inovačních
výkonů mezi zeměmi. Výsledky druhé fáze ukazují, že inovativní vůdci jsou obecně efektivní ve využívání finančních
a kognitivních zdrojů k tvorbě intelektuálního kapitálu. Součástí řešení jsou i doporučení na zvýšení efektivnosti v
dalších regionech EU.
Klíčová slova
efektivnost, znalostní kapitál, DEA analýza
JEL classification: O34, P35
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1. Introduction
Innovation in the knowledge-based economy is dependent on the quality knowledge generation capacity within
the economic ecosystem of countries in contemporary times. Intellectual capital comprises of diverse forms of
intellectual material that facilitates growth and value creation for nations and firms. Intellectual Capital can be
likened to knowledge capital and other intangible resources which put together constitute the intellectual assets of
a nation or an organization (Guthrie, 2001). Kianto et al. (2014) defined Intellectual Capital as putting together all
intangible and knowledge-related resources needed and used by an organization’s productive processes to create
value for consumers. Pulic (2008) concurs with Kianto et al. (2014) and mainly attributed IC to those individuals
or employees with the abilities to combine and transform knowledge into product and services which create value
for the nation or company.
In the era of the industrial economy, a nation’s or company’s wealth was determined by its ability to ensure mass
production of goods and services. However, in the contemporary epoch, the production of knowledge has become
predominant in what most scholars refer to as the knowledge economy. Economic value is created not just through
the quality of production rather, by the knowledge of the human resource (Kai Wah Chu et al, 2011) hence,
knowledge production is imperative for competitive advantage Pulic, (2008). Likewise, continuous improvement
in all business aspect is dependent on the knowledge, talent, and creativity of people. A growing number of
researches has found a strong correlation between IC and economic (business) performance (Firer, Williams, 2003;
Shiu, 2006; Ze´ghal, Maaloul, 2010; Komnenic, Pokrajčić, 2012; Vishnu Kumar Gupta, 2014; Kai Wah Chu, Hang
Chan, Wu, 2011) which ultimately determines the regional innovation performance.
We are of the view that strong innovative performance of any country can be attributed to the efficient generation
of intellectual capital and also by the support from the government geared towards innovation and research through
research and development expenditure of government research institutes and public institute of higher learning.
Carayannis, Grigoroudis, and Goletsis (2015) using a multi stage and multi-level analysis found large differences
regarding the efficiency scores of the different stages and levels of 23 selected EU member states revealing the
presence of significant divergences from the expected norm concerning. Afzal (2014) also investigated national
innovation systems’ input–output components of 20 developing countries with both DEA analysis and TOBIT
regression. He eventually suggested that secondary school enrolment ratio; the labour force (ages 15–65), as a
percentage of the total population; and domestic credit expansion by the business sector, as a percentage of GDP
should be focused on if innovation is to be improved. However, our research adds to the this collection by using a
multi-stage DEA analysis to assess the efficiency of intellectual capital generation of innovation leaders and strong
innovators revealing their core and intermediate process (in)efficiency.
Thus, the main purpose of the research is to comparatively compute the intellectual capital generation efficiency
of innovative leaders and strong innovators and also reveal the driver of such efficiency or inefficiency. These
groups, consisting of 10 countries, were focused on as they occupied the highest ranks on the innovation scores on
the European Commission (2018) as we are of the view that efficient scores of intellectual capital generation is a
antecedent of higher innovation. The remaining sections are organized as follows; the review of relevant literature
on intellectual capital which outlines the various aspect of the IC concept follows right after the introductory part.
Necessary questions are raised and gaps in the literature stock identified which our empirical analysis will seek to
answer and fill the void.
2. Literature review
The European Innovation Scoreboard classifies human resources, excellent and open research systems as well as
finance and support as enablers for innovation performance. This means that human resource, the custodians and
repository of knowledge of the firm intellectual assets is key if high innovation results must be achieved.
Intellectual Capital is defined as the ownership of the knowledge, experience, technology, skills as well as better
customer relation that enable a company to gain a competitive notch in its business sector. Zeghal and Maaloul
(2010) state that Intellectual Capital consists of all the company knowledge employed in the firm’s businesses to
generate value. Alipour (2012) on the concept of Intellectual Capital, defines it as the intangible knowledge assets
rooted in the company, He further classifies Intellectual capital as comprising of the intellectual competences,
resources, and property of the organization. In most Intellectual capital literature, the acceptable classification of
IC consists of three main elements, which include; human capital (HC), structural capital (SC) and relational
capital (Bhuyan, 2015 and Alipour, 2012, Ornek & Ayas, 2015). Moreso, Schiuma et al. (2008), in addition to the
world-wide accepted classification, included organizational capital and social capital to the list whilst naming the
relational capital as stakeholder capital to encapsulate the broader perspective of the stakeholder view of the firm
ownership. In contrast, the OECD classifies IC as comprising of two main aspects that are; Human Capital and
Structural Capital (OECD, 2000). Slarter and Narver (2000) suggest that knowledge (intellectual capital) is
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generated through four main generic strategies. Amongst them is the collaborative knowledge generation strategy
which involves the creation of intelligence mainly with and from other organizations. Through research
expenditure in the business and public sector as well as investment by venture capitalist, the innovation
environment is created to enable firms to collaborate for generating intellectual capital. Intellectual capital creation
is as necessary for a firm’s financial performance and growth as the nation’s competitiveness in the global
economy. This notion is enshrined in the Europe 2020 agenda for smart growth based on knowledge and
innovation. It is based on this idea that the nations of the European Union are ranked on the innovation scoreboard.
Firms generate new knowledge through various activities and interactions which result in knowledge co-generation
(Carayannis, Grigoroudis, 2016).
Most empirical studies have found that intellectual capital has a positive relationship with innovative performance.
Xu and Wang (2018) in their paper assessing IC, financial performance and sustainable growth of Korean
companies found that Intellectual Capital improves firm financial performance aiding in wealth creation in South
Korea at the same time affecting sustainable growth of businesses. In the Chinese context, Yuqian and Dayuan,
(2015) assess the effect of intellectual capital on innovative performance and found that the former influences the
latter positively mediated by knowledge-based dynamic capabilities. In a case study of Italian SMEs, Mazro &
Scarpino (2016) find a rather interesting result of the collaboration for an intellectual capital generation where the
SMEs’ external stakeholders constitute the main source for intellectual capital improvement. Jordao & Almeida
(2017) analysed the influence of Intellectual capital on long term corporate performance of Brazilian companies
and find that intellectual capital systematically increases financial performance in the long round. Bontis warned
that despite the massive proliferation of the intellectual capital concept which has spurred more intellectual capital
creation, its usability, and frequent usability is paramount else, the knowledge created may fade out (Bontis, 1999).
As shown by the numerous empirical works of literature reviewed, most researches focus on the effects of
intellectual capital on innovation performance and firm profitability. We refer to these studies as focusing mainly
on the output and effects with the exception of the European Innovation scoreboard which has created
comprehensive indicators to measure yet the output performance of EU countries, it does gives us the motivation
to explore the efficacy of the enabler indicators that contribute to the overall innovation performance of the EU
strong innovators and innovative leaders using a Data Envelopment Analysis to compare these groups. The
research seeks to answer this research question:
 How efficient do strong and innovative leaders generate intellectual capital for innovation performance?
 Do innovative leaders have generally higher efficiency scores? If yes, why?
3. Data and Methodology
The research resorted to the use of data from the European Commission (2018). This data represents a yearly
comprehensive analysis of the innovation performance of European Union countries revealing their strengths and
weaknesses in intellectual capital creation. The survey ranks countries according to their relative scores with the
EU average of innovation thereby classifying them as innovation leaders, strong innovators, moderate and modest
innovators. The data focused on countries that had featured among the innovation leaders and strong innovators
summing up to ten (10) countries. They were selected because the research wanted to focus on highly innovative
countries and how efficient they perform in generating intellectual capital and whether their essentially high
innovation scores are connotes with their efficiency. The variables were also selected in line with A collection of
inputs and countries selected is thereof presented in Table 1.
Table 1: Selected country and variables for the research.
Innovation
leaders
Strong
Innovators
Inputs
Variables
Intermediate
variables
Output
variables
Finland (FI) Germany (DE)
Public R&D
expenditure (PR&D)
SMEs collaboration
with others. (SMEColl)
Patent applications to
the EPO office (PAT)
Sweden (SW) France (FR)
Business R&D
expenditure
(BR&D)
Public private
publications
(PPP)
Trademark applications.
(TRD)
United
Kingdom (UK)
Luxembourg
(LU)
Venture Capital
(VCI)
Denmark (DK) Netherlands
(NL)
Austria (AT)
Belgium (BE)
Source: European Commission (2018)
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Regarding the analytical tool assessed the used of parametric and non-parametric test of efficiency and we opted
for a non-parametric test of efficiency Among several parametric and non-parametric techniques, Data
Envelopment Analysis (DEA) has been widely applied to measure the relative efficiency of multiple decision
making units (DMUs) that transform multiple inputs to multiple outputs in a similar context. The fact that, unlike
parametric models, DEA does not require an explicit function that relates inputs to outputs is considered to be one
of the main advantages of DEA. Data envelopment analysis was used as the tool of analysis of the efficiency at
which intellectual capital is generated by countries. Thus, DEA models focus exactly on input-output efficiency
of innovation systems, where each country or region is considered as an independent DMU (Cao 2011).
To capture the various stages of intellectual capital generation as used by (Carrayannis et al. 2015, Kao 2014) and
not to assume a linear production function for patent and trademark generation, the research employed the multi
stage innovation process that captures the introductions of variation of capital, the cooperation tendencies or output
required and the eventual output to be measured. Based on the elements of European Commission (2018) and the
work of Carrayanis et al (2015), Afzal (2014) and other literature on intellectual capital, we created a model for
the research as shown in Figure 1 below. The research follows the structure below as shown in Figure 1 below. To
acquire the efficiency scores, we opted for the multiplicative instead of the additive method since this more
accurately reflects the scores of the Decision making Units (DMU’s). In applying the DEA model, we opted for
the CCR (Charnes, Cooper and Rhodes) model. The BCC model is based on the CCR (Charnes, Cooper and
Rhodes) model. The CCR model assumes constant returns to scale and produces efficiency scores of up to one (1).
A DMU with a score of less than is regarded as inefficient where as a score of up to one (1) is regarded as efficient.
Fig. 1: Theoretical Model of DEA analysis of
Source: Based on the literature Carrayanis et al. 2015; Afzal 2014; European Commission (2018)
3.1 Data Envelopment Analysis (DEA): Multi-stage process
As noted by several scholars, the two stage modelling is a rather simplified approach which could potentially help
draw a line between innovation and invention and also enable the comprehension of complexity of intellectual
capital generation process (Kai 2014, Carrayannis et al. 2015). Many authors pre suppose that the study of
innovation in isolation can give misleading results (Carayannis, Provance, 2008; Cruz-Cázares, Bayona-Sáez,
García-Marco, 2013, Chilingerian, Sherman, 2011; Ebrahimnejad, Tavana, Lotfi, Shahverdi, Yousefpour, 2014;
Kao, Hwang, 2008; Matthews, 2013) because costs regarding some inputs now may have already been incurred.
Furthermore, the European Union document on diffusion of innovation in 2009 duly recognizes cooperation as the
very significant catalyst in the creation of intellectual capital. Hence, the existence of a complex knowledge
production function in the intellectual capital generation process, captures the level of raw capital input, the
catalytic process and expected output generated similar to Mode 3 function by Carayannis, Campbell, (2009). Even
more, given the existence of several involved actors, (universities, research institutions, business enterprises,
governmental organizations, etc.), we consider intellectual capital generation as an interactive, networking and
collaboration process rather than a linear generation. Efficiency scores calculated at the first stage are multiplied
with the efficiency scores at the second stage.
4. Results and Analysis
Below is the descriptive statistics of the innovation scores of the countries selected for the efficiency analysis.
Public R&D
expenditure
Business R&D
expenditure
Innovative SMEs
collaboration
Pubic Private co-publications
Venture Capital
Trademark applications
Patents applications to the
EPO office
Initial Inputs Intermediate inputs Outputs
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Tab. 2: Descriptive statistics of countries selected.
PR&D VCI BR&D SMECOLL PPPUB PAT TRD
Valid 10 10 10 10 10 10 10
Missing 0 0 0 0 0 0 0
Mean 117.350 125.190 138.020 148.140 140.370 136.430 149.400
Std. deviation 27.794 57.158 45.678 49.611 35.161 55.927 52.421
Minimum 64.600 63.300 51.900 80.000 79.500 47.500 90.300
Maximum 146.000 205.500 193.500 217.400 201.300 223.700 278.700
Source: Authors’ analysis of European Commission (2018)
Using data from the European Commission (2018), at constant returns to scale, we run a multi-stage DEA analysis
of innovation leaders and strong innovators in their efforts to generate intellectual capital measured as patent
applications and trademark applications submitted to the European Patent Office. In the diagram below, we
provided a relative efficiency scores at both stages and the eventual efficiency score of the countries considered
using DEA technique.
Fig. 2: DEA analysis of Innovation leaders and Strong
Source: Author’s own Computation.
From Figure 2, it could be seen that in the first stage of innovation analysis produced mixed results as the
innovation leaders together with the innovation followers recorded higher than one another. France, Germany,
Finland and Sweden were revealed to be the countries that were relatively inefficient in the production of
intellectual capital. This implies that when we consider the inputs of venture capital, public and business
expenditure invested in public and private institutions, in relative terms, these countries have less efficient
processes of utilization of these input resources. This could be interpreted in the context of causality such that
invested may not be entirely oriented to research or outright misapplication or inefficiency of resource utilization
even though firm investments may be the weakest strength of France and Sweden unlike Germany. These findings
of especially Austria, Belgium, Denmark and Finland are contrary to the findings Pan, Hung and Lu (2010) who
researched on Asian and European countries to measure magnitude of performance difference; however, it
concurred the results of Cullman, Schmidt-Ehmcke, Zlocyzsti (2009) who researched on the relative efficiency of
knowledge production in OECD countries.
Results of second analysis reveals only 3 relatively efficient countries, two of which are innovation leaders, making
up 75% of the pie. Although Sweden and Denmark were the next closely related countries, largely the rest of the
inefficient countries were composed of the strong innovators. This goes to imply that countries with higher
innovation performance equivalently exhibits an efficient usage of the inputs, that is firm- to- firm cooperation as
well as public private sectors collaboration in publications of research results. This also largely shows that firms
that firms-to-firm cooperation is not adequately harnessed among the less efficient countries, namely, Belgium,
Austria, Netherland, France, Denmark and Sweden even as cooperation has been largely publicized as a strong
mediator and antecedent in creation of intellectual capital. These findings are concurred by the work of Broekel,
Buerger, Brenner (2015), Broekel (2012) and the ideas Cai (2011) who studied innovative efficiency of 22
1.00  1.00  1.00 
0.86 
0.60 
0.65 
0.95 
1.00 
0.87 
1.00 
0.73 
0.50 
0.80 
1.00 
0.77 
1.00  1.00 
0.74 
0.98 
0.53 
0.73 
0.50 
0.80 
0.86 
0.46 
0.65 
0.95 
0.74 
0.84 
0.53 
 0.40
 0.50
 0.60
 0.70
 0.80
 0.90
 1.00
 AT  BE  DK  FI  FR  DE  LU  NL  SW  UK
 1st stage  2nd stage  Final Efficiency Score
XXII. mezinárodní kolokvium o regionálních vědách Sborník příspěvků Velké Bílovice 12.–14. 6. 2019
 
182
countries; he recommended that market environment, governance, education system and even ICT infrastructure
affects regional capacity to be innovative.
Final efficiency scores of the countries assessed at constant returns to scale using the multiplicative model revealed
that even though none of the countries assessed was found to be efficient, the countries that obtained the highest
portion of efficiency scores were largely from innovation leaders namely, Luxembourg, Finland, Sweden and
Denmark as the top four member states bar United Kingdom which was next to the two lowest efficient countries,
Belgium and France chronologically. This implies that despite the strengths and weakness of countries innovation
poles, they may always find means of notching up their innovation targets but cannot underestimate the need for
cooperation intensity as proved by results and other studies mentioned above. Eventual efficiency score reveals
80% of the innovation leaders formed the top 5% of the efficient countries at constant returns to scale in the
generation of intellectual capital. Inadvertently revealing innovation leaders is largely more efficient in intellectual
capital generation than strong innovators at a constant return to scale. Results of research also further concurs the
work of Cai (2011).
5. Conclusion
The research set out to assess the efficiency scores and performance of innovative leaders- Finland, Sweden,
United Kingdom, Denmark and strong innovators- Germany France, Luxembourg, Netherlands, Austria, and
Belgium. Using the multi-stage DEA approach commercialized by various authors with the objective of capturing
all levels of knowledge production processes we conducted the research in this mold. Initial stage analysis
conducted portrayed public research and expenditure, venture capital and business research and expenditure as the
initial inputs whilst intermediate inputs, SMEs collaboration and public and private sector publication were held
as the output variables; intermediate inputs were thereon used as inputs in the second analysis and the scores for
both stages were multiplied to acquire the total efficiency score.
The first stage analysis that postulated financial investments as basic inputs revealed mixed efficiency scores
between both the innovative leaders and strong innovators as their efficiency scores such that out of the four, only
Denmark and United Kingdom had efficient scores making up 50% of their rank whilst Austria, Belgium and
Netherland also had efficient scores for the strong innovators. Second stage of the analysis focused on primarily
on cooperation of SMEs and the public and private sector. In that Innovative leaders formed 75 % of the efficient
member states making up three (3) out of the four (4) efficient countries. Eventual efficiency scores also revealed
innovative leaders with the highest efficiency even though no country was found efficient. Luxembourg , Finland,
Sweden and Denmark all innovative leaders occupied the top 4 most efficient countries effectively revealing
innovative leaders as more efficient as intellectual capital generation than strong innovators. The question of what
makes them more efficient can be drawn from the second analysis which revealed the innovative leaders operating
with more efficiency when we measured basically SMEs collaboration and public private co-publications,
essentially cooperation. Cooperation seems to be the strongest factor that cuts across all innovative leaders even
though countries may have different regional strength; it has even further been buttressed by the most researchers
and the European Commission as well.
We recommend that not a drastic shift but a focus on not just firm-to-firm cooperation and firm-to-academia but
a more concerted effort to hinge and organize efforts to create patents and trademarks on openness of private,
public, Universities and NGOs as well. We also recommend healthy benchmarking practices among countries and
a more active interactions at all levels of public organizations and private ventures despite the need to also compete
for the good of the countries concerned and European Union eventually.
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This work was supported by grant provided by the scientific research project of the Czech Sciences Foundation
Grant No: 17-11795S.