The Analysis of the Innovation Potential of the Czech Higher ...

The Analysis of the Innovation Potential of the Czech Higher Education: An Application
of Porter’s Model of Competitive Advantage
Anna Vitásková & Josef Jukl

Abstract. A successful knowledge-driven society is based on its ability to produce knowledge
and innovation through a maximal utilization of disembodied human capital. In this sense, all
major public institutions of higher education are expected to play a key role. Yet, when we
examine the extent in which the Czech system of higher education develops and supports a
high level of disembodied forms of human capital, several issues raise immediately. First,
despite many governmental efforts, the Czech system of higher education still belongs among
the so-called post-Soviet model of higher education that tends to be a hindrance to the
effective production of knowledge. Second, a little evidence is available on the university
research production and its impact on innovation potential of the Czech Republic. Therefore,
the purpose of this research paper is to examine the effects of university research on the
overall innovation potential of the Czech Republic.

Keywords: bibliometrics, citation impact, Czech system of higher education, disembodied
human capital, innovation potential, university-industry collaboration

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Introduction

Let us start by stating the obvious: maximal utilization of “disembodied human
capital” (Romer, 1994) is one of the major prerequisites for the development of innovation
potential of a society (see for example, Lundvall, 1992; OECD, 1999; Rebelo, 1998; Romer,
1994; World Bank, 2002). Innovation potential can be broadly defined as having the ability
and capacity “to manage knowledge creatively in response to market-articulated demands and
other societal needs” (OECD, 1999, p. 9). Disembodied human capital refers to the stocks of
economically and socially useful knowledge that does not reside specifically in trained
individuals, but instead exists indepedently of the individuals who created it. In this context, a
research-intensive and competitive knowledge society expects its national systems of higher
education to produce world-class research leading to the continuous ability of a nation to
compete1 on international markets.

The Czech Republic, which at one point was widely praised in the West for a
relatively smooth transition from communism to capitalism, has been considered one of the
few post-socialist European countries with the best growth and innovation prospects (OECD,
2000). Yet, according to the renowned Global Competitiveness Report, the Czech Republic’s
overall growth competitiveness ranking has declined steadily from 31st place in 2000 to 37th
place in 2003 (World Economic Forum, 2004). This report also includes the ranking of
innovation growth and innovation potential; the Czech Republic ranks on 40th place in the
former and on 45th place in the latter (Table 1).
Table 1: Innovation Growth and Innovation Potential Index

Innovation Growth
Innovation Potential
Countries
2003 2004 trend 2004
USA 2
2

1

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3
Finland 1
1

3
Estonia 22
20
+
26
Hungary 33
39
-
38
Lithuania 40
36
+
30
Czech Republic
39
40
-
45
Source: The Global Competitiveness Report 2004-2005

The fact that the Czech Republic scores approximately in the middle of the ranking might not
seem as alarming unless we put it in the comparative context and discover that countries,
which traditionally used to score lower than the Czech Republic, such as Poland, Estonia,
Hungary, Lithuania, are now scoring higher than the Czech Republic.

One of the major causes of the decline in the innovation potential of the Czech
Republic repeatedly points towards the low productivity and effectivity of higher education
system, especially in the arena of research (see for example, Kaderabkova, 2004; Komarek,
2005; Vysoka-Vitaskova, 2004). Having said that, a fundamental question pertaining to the
Czech higher education arises immediately: To what extent, this system develops high level of
disembodied human capital? Or in other words, what is the impact of research activities of
Czech universities on innovation potential?

In order to answer these questions and to analyze the role of the Czech higher
education in building innovation potential, we selected Porter’s “diamond” model of
competitive advantage as our guiding theoretical lens. Thus, in this paper, we first examine
Porter’s four factors influencing competitive advantage in the sector of Czech higher
education. Then, we detail and describe our research methodology as well as sources of data.
And finally, we discuss the main empirical findings through the application of the Porter’s
model.

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Theoretical Framework: Porter’s “Diamond” Model of Competitive Advantage

The theory of competitiveness, generally known as competitive advantage, has
dimished the relevance of some basic premises of macreconomic theories. The traditional
Kenysian theories do not take into consideration growing influence of microeconomic factors
and conditions necessary for creating innovation-driven growth upon which competitiveness
is essentially based. A variety of approaches to competitiveness has produced a myriad
definitions of the concept, however, an overwhelming majority of these theories focus only on
one aspect of competitiveness, for instance, input-output side, organizational processes,
competitive performance, which provide very rich, yet somewhat narrow and limited picture
often leading to weak understanding of competitiveness and subsequent creation of
suboptimal policies (see for example, Buckley et al., 1990; Hamalainen, 2003;
Hatzichronoglou, 1996; Nelson, 1992).
Therefore, one of the more complex theories emphasizing non-fiscal dimension of
competitiveness that explains why some countries outperform others at economic activities is
Porter’s “diamond” theory of competitive advantage of “companies in a nation” (Porter,
1990). The theory maintains that while such sound factors of macroeconomic policies as
creation of a stable political environment, a trusted legal framework, and improvement of
social conditions are indeed necessary to ensure a prosperous economy, they are not
sufficient. Competitiveness ultimately depends on continuous improvement of the so-called
microeconomic foundations of competition. Porter (1990) defines those as four interrelated
and mutually dependent factors: (1) factor (input) conditions; (2) demand (output) conditions;
(3) contextual (corporate) strategy and structure; and (4) related and supporting industries. In
addition, his framework also includes additional two elements of government and chance
which may also influence national competitiveness through the four main factors.

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While this model is initially intended to assess the conditions of competitiveness of
local industries, it has proven to be useful in the context of higher education as well. Curran
(2000), for instance, applied this model to the assessment of research performance in the
discipline of geography in the U.K. Hazelkorn (2004), on the other hand, applied the model to
illustrate the complex (and often problematic) relationship between government and
universities. Based on some of Curran and Hazelkorn’s applications, Figure 1 shows our
adaptation of Porter’s model of competitive advantage to our conceptual framework.
Figure 1: Adaptation of Porter’s Model of Competitive Advantage to Higher Education
System
COLLABORATIVE
PARTNERSHIPS

The level of involvement in
university-industry collaborative
Chance
efforts.
INPUT (FACTOR)
OUTPUT (DEMAND)
CONDITIONS
CONDITIONS


The system‘s ability to
Relevance, interest,
provide adequate
and impact of the
resources for
academic research.
research production.

ORGANIZATION OF HIGHER
Government
EDUCATION SYSTEM

The structure of the system allowing
for flexibility and responsiveness.

Source: Modified from Curran, (2000); Hazelkorn (2004); Porter (1990).

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In our context of analyzing the research impact of the Czech universities on innovation
potential as a prerequisite for building true competitiveness, input conditions are defined as
the system’s ability to provide adequate resources conducive to the production of
economically and/or socially relevant research. Among these resources belong financial (i.e.,
research and development funding) and human resources (i.e., research and development
personnel). Due to their tangible nature, the assessment of input related factors allows to
identify possible gaps between research and development (hereafter R & D) expenditures and
the actual sectoral (i.e., higher education system) performance by uncovering inadequate
infrastructure and possible weak research competencies (Geisler, 2000).

Second, output (or demand) conditions are measures of relevance and impact of
university research on a society (Curran, 2000; Hazelkorn, 2004). Geisler (2000) distinguish
between two different kinds of impact: (1) indirect or non-tangible impact, such as improved
reputation of universities, impact on student satisfaction, contribution to the public good, etc.;
and (2) direct or tangible impact as explicated by the number of publications, citations,
patents, etc.
Third,
organizational
structure is defined as the flexibility and responsiveness of the
system to respond to the market and societal needs, thus building the innovation potential.
Hence, the critical stimuli for innnovation actually come from the specific nature of the
institutional context of each system or country (Dosi, 1988). Gibbons and associates (1994) in
their influential analyses of knowledge production describe a new form of institutional
structure where transdisciplinary research (as opposed to discipline-specific research) is
produced in the context of supply and demand, evaluated by its immediate stakeholders (as
opposed by peer review judgments), and from its beginning is intended to be useful to the
stakeholders (Gibbons et al., 1994). Organizationally, therefore, such universities - often
called entrepreneurial - are becoming more heterarchical (as opposed to hierarchical) with

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greater flexibility and willingness to disperse research results faster into a society (i.e.,
socially distributed knowledge) (see for example, Etzkowitz & Leydesdorff, 1997; Gibbons
et al., 1994; Slaughter and Leslie; 1997).

And finally, regional and international relations refer to the level of university
involvement in collaborative networks and partnerships with other knowledge-creating public
and/or private institutions. Currently, there are two contrasting models of university-industry
collaboration in the knowledge production: (1) “knowledge flows”; and (2) “triple helix”
(Etzkowitz, 1998). The first model is a linear, one-directional model of “demand pull” or
“technology push” based on a relative separation of partners involved (see Figure 2).
According to this model, government typically determines the research priorities (i.e., top-
down identification) without consulting with smaller organizations, institutions, private
enterprises or other relevant stakeholders.
Figure 2. Knowledge Flow Model of University-Industry Collaboration

Government



University

Industry


The second model known as the “triple helix” takes the traditional forms of
institutional differentiation among universities, industries, and government as the starting
point for institutional interaction in the research production (Etzkowitz & Leydesdorff, 1997)
(see Figure 3). The rationale for this cooperation is relatively simple: industry profits by easy
access to research results and human resources, and universities have some of their research
financed. The role of government in this institutional collaboration is one of support; it puts in

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place necessary infrastructure for successful cooperation between universities and industry
and eliminates barriers that can potential hinder such partnership.
Figure 3. Triple Helix Model of University-Industry-Government Relations


Government




Universities
Industry





Hybrid
Knowledge

producers



This model, in contrast to the previous one, is characterized by a high level of
entrepreneurial activities on the part of university academicians, such as networking, strategic
partnering, and requiring academicians to also possess strong leadership skills in managing
multidisciplinary projects.

Therefore, by adapting holistic Porter’s model of competitive advantage where all the
factors are not only equally important to innovation process, but also mutually dependent, we
propose to answer the following set of research sub-questions: (1) Does the system of higher
educaton provide adequate resources for research? (2) What type of research (basic or
applied) the Czech system of higher education predominantly produces? What are the effects
university research on an international scholar community? (3) To what extent, the current
structure of higher education responds to the increased demand for economically useful
research? (4) Is there any collaboration between Czech universities and other research-
producing entities? Even though, the empirical focus is on the Czech Republic, we also
employ, statistical data pertaining to the closest Czech competitors, such as Poland, Hungary,

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Slovenia, as well as we use data on Finland and the U.S., which represent the most successful
countries in developing their innovation potential; thus are used as benchmarks.
Research Methodology and Data

Our research methodology utilized the most frequently used approach in quantitative
innovation studies; the use of indirect proxies for the assessment of university research impact
on innovation potential2. In order to obtain the most holistic picture, we analyzed macro as
well as micro data. Macro data came from EU, OECD statistical databases, and the Institute
for Scientific Information Thomson Scientific (hereafter ISI) and the micro data came from
the 2005 Survey of Faculty Members of Czech Higher Education Institutions3 (see Table 2 for
summary).
Table 2: Summary of Indicators and Sources
Factor
Types of Indicators
Source of Data
Input Conditions
R & D expenditure in PPS
Eurostat
shares of R & D funds
OECD
R & D personnel (FTE) Eurostat

Output
types of research survey
Conditions
number of publications ISI
Thomson
number of citations
ISI Thomson
number of patent applications
survey
System Structure
management & leadership of university
survey
& department
information sharing
survey
academic freedom
survey
Collaboration
number of consultancies
survey

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perceived
barriers

survey


To analyze the effects of the input conditions related metrics, we selected two major
proxy measures of innovation potential: R & D expenditure expressed in purchasing power
standards4 (hereafter PPS) and R & D personnel (OECD, 1994). In terms of R & D personnel
measures, there are two possible approaches: headcount or full-time equivalence (hereafter
FTE). The first approach refers to measurement of either the total number of persons
employed in R&D on a given date or during the year or the average number of persons
employed in R&D during the year (OECD, 1995). The FTE, on the other hand, refers to the
proportioned measurement of a researcher5 and his/her time spent on R&D activities (OECD,
1995). Since headcount is best to use for construction of additional macro-indicators, which
was not the aim of this study, we selected FTE measurement as more appropriate for our
particular context.

For the measurement of the output conditions, we followed Jaffe’s (1998) conceptual
framework and assessed the number of patents, the number of publications and citations as
our proximal outputs. For this endeavor, we utilized a method known as bibliometrics.
Bibliometrics refers to the evaluation of scientific and technical published outputs from
science and its related disciplines. It is based on the summation of the count of publications in
a selected academic field which serves as a foundation for subsequent calculations of the
relative influence of those publications (i.e., citation impact). Data were collected from ISI
Social Science Citations Index, Science Citation Index, and Arts and Humanities Citation
Index. Due to the unavailability of some data, the time frame of the collected data differs; the
comparative bibliometric evaluation of selected countries covers a period between 2000-2005,
while the bibliometric evaluation of the Czech universities covers a period from 1990-2005.

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