Volume 6
anagin
Managing
ISSN I581-63II
Global
Boštjan Antončič
International
ir n R^V^fy*
[vJ^cIllvl
journal
Managing Global Transitions
International Research Journal
EDITOR
Boštjan Antoncic, University of Primorska,
Slovenia
ASSOCIATE EDITORS
Egon Žižmond, University of Primorska,
Slovenia
Dušan Lesjak, University of Primorska,
Slovenia
Anita Trnavčevič, University of Primorska,
Slovenia
Roberto Biloslavo, University of Primorska,
Slovenia
EDITORIAL BOARD
Zoran Avramovič, University of Novi Sad,
Serbia
Terrice Bassler Koga, Open Society Institute,
Slovenia
Cene Bavec, University of Primorska, Slovenia
Jani Bekö, University of Maribor, Slovenia
Vito Bobek, University of Maribor, Slovenia
Štefan Bojnec, University of Primorska,
Slovenia
Suzanne Catana, State University of New York,
Plattsburgh, usa
David L. Deeds, University of Texas at Dallas,
USA
David Dibbon, Memorial University
ofNewfoundland, Canada
Jeffrey Ford, The Ohio State University, usa
William C. Gartner, University of Minnesota,
USA
Tim Goddard, University of Calgary, Canada
Noel Gough, La Trobe University, Australia
George Hickman, Memorial University
of Newfoundland, Canada
Robert D. Hisrich, Thunderbird School of
Global Management, usa
Andras Inotai, Institute for World Economics
of the Hungarian Academy of Sciences,
Hungary
Hun Joon Park, Yonsei University, South Korea
Štefan Kajzer, University of Maribor, Slovenia
Jaroslav Kalous, Charles University,
Czech Republic
Maja Konecnik, University of Ljubljana,
Slovenia
Leonard H. Lynn, Case Western Reserve
University, usa
Monty Lynn, Abilene Christian University, usa
Neva Maher, Ministry ofLabour, Family and
Social Affairs, Slovenia
Massimiliano Marzo, University of Bologna,
Italy
Luigi Menghini, University of Trieste, Italy
Marjana Merkac, College of Entrepreneurship,
Slovenia
Kevin O'Neill, State University of New York,
Plattsburgh, usa
David Oldroyd, Independent Educational
Management Development Consultant,
Poland
Susan Printy, Michigan State University, usa
Jim Ryan, University of Toronto, Canada
Mitja Ruzzier, University of Primorska,
Slovenia
Hazbo Skoko, Charles Sturt University,
Australia
David Starr-Glass, State University of New
York, usa
Ian Stronach, Manchester Metropolitan
University, uk
Ciaran Sugrue, Dublin City University, Ireland
Zlatko Šabic, University of Ljubljana, Slovenia
Mitja I. Tavcar, University of Primorska,
Slovenia
Nada Trunk Širca, University of Primorska,
Slovenia
Irena Vida, University ofLjubljana, Slovenia
Zvone Vodovnik, University of Ljubljana,
Slovenia
Manfred Weiss, Johan Wolfgang Goethe
University, Germany
Min-Bong Yoo, Sungkyunkwan University,
South Korea
Pavel Zgaga, University ofLjubljana, Slovenia
EDITORIAL OFFICE
University of Primorska
Faculty of Management Koper
Cankarjeva 5, SI-6104 Koper, Slovenia
Phone: ++386 (0) 5 610 2021
Fax: ++386 (0) 5 610 2015
E-mail: mgt@fm-kp.si
www. mgt.fm-kp.si
Managing Editor: Alen Ježovnik
Editorial Assistant: Tina Andrejašic
Copy Editor: Alan McConnell-Duff
Cover Design: Studio Marketing jwt
Text Design and Typesetting: Alen Ježovnik
Managing Global Transitions
International Research Journal
VOLUME 6 • NUMBER 3 • FALL 2008 • ISSN I58I-63II
Table of Contents
227 The Editor's Corner
229 The Relationship Between Entrepreneurial Intensity
and Shareholder Value Creation
Pierre Erasmus
Retha Scheepers
257 The Relationships Among Leadership Styles, Entrepreneurial
Orientation, and Business Performance
Chung-Wen Yang
277 How Internal and External Sources of Knowledge Contribute
to Firms' Innovation Performance
Anja CotiC Svetina
Igor Prodan
301 Ensuring Professionalism of the External Evaluation Commission:
The Slovenian Case Study
Karmen Rodman
Nada Trunk Širca
317 An Empirical Analysis of Credit Risk Factors
of the Slovenian Banking System
Boštjan Aver
The Editor's Corner
At Managing Global Transitions we are proud to present you yet an-
other number of our journal, which in this issue covers the topics of
entrepreneurial intensity, leadership styles, sources of knowledge, exter-
nal evaluation, and credit risk factors. Our main area of focus continues
to remain the transition research. In addition to this mgt emphasizes
the openness to different research areas, topics, and methods, as well as
international and interdisciplinary research nature of scholarly articles
published in the journal.
The current issue begins with a paper written by Pierre Erasmus
and Retha Scheepers, who investigate the relationship between the en-
trepreneurial intensity and the shareholder value created by analyzing
the data from South Africa. In the second paper, Chung-Wen Yang ex-
amines the effects of leadership style on the development and imple-
mentation of entrepreneurial orientation in small and medium-sized
companies in Taiwan. In the third paper, Anja Cotic Svetina and Igor
Prodan study the influence of knowledge sources on innovative perfor-
mance which is based on the data from in-depth interviews with top
executives of manufacturing firms in Czech Republic, Germany, Italy,
Poland, Romania, Slovenia, and the United Kingdom. In the fourth pa-
per, Karmen Rodman and Nada Trunk Širca investigate the professional
competences of the eternal evaluation commission members in Slove-
nian higher education. In the last - fifth paper, Boštjan Aver presents the
results of the analysis of credit risk factors of Slovenian banking system.
Boštjan Antoncic
Editor
The Relationship Between Entrepreneurial
Intensity and Shareholder Value Creation
Pierre Erasmus
Retha Scheepers
Innovation and entrepreneurship have long been regarded as sources
of value and wealth creation. Previous research has shown that there is
a positive relationship between enterprises' levels of entrepreneurship
and their financial performance. Little research, however, has hitherto
focused on measuring the relationship between entrepreneurship and
shareholder value creation. In this study the relationship between the
entrepreneurial intensity and the shareholder value created by an en-
terprise is investigated. An adapted corporate entrepreneurship (ce)
measurement instrument is applied in order to gauge entrepreneurial
intensity, while shareholder value creation is measured by the market-
adjusted total share return (tsr) and the value based financial per-
formance measure Economic Value Added (eva). The study is con-
ducted for a sample of enterprises listed in the industrial sector of
the Johannesburg Securities Exchange (jse) for the period 2003-2005.
The contribution of the study is the focus on the relationship between
entrepreneurial intensity and shareholder value creation, rather than
purely on the accounting-based financial performance of an enterprise.
Key Words: entrepreneurial intensity, value based financial
performance measures, economic value added
jel Classification: L25, L26
Introduction
Innovation and entrepreneurship have been emphasised in recent years
by the popular business press, corporate leaders and academics (Hamel
and Breen 2007; Hof 2004; Planting 2004; Covin and Slevin 1991; Lump-
kin and Dess 1996; Leibold, Probst, and Gibbert 2002). The potential of
entrepreneurship to create value for various stakeholders (Morris 1998)
and value for shareholders (Vozikis et al. 1999) has heightened academic
Dr Pierre Erasmus is a Senior Lecturer at the Department of Business
Management, University of Stellenbosch, South Africa.
Dr Retha Scheepers is a Lecturer at the Department of Business
Management, University of Stellenbosch, South Africa.
interest in the field. A number of studies have investigated the relation-
ship between the level of corporate entrepreneurship (ce) of an enter-
prise and its financial performance (Wiklund 1999; Wiklund and Shep-
herd 2003; 2005; Zahra 1991; Zahra and Covin 1995). However, most
of these studies have concentrated on traditional accounting measures
adapted from previous studies. The measures used in many of these
studies did not build on a theoretical rationale of economic and en-
trepreneurship theory, despite the growing recognition of 'the impor-
tance of theory-based measures to entrepreneurship research' (Vozikis et
al. 1999,33).
Economic theory holds that firms exist to maximise value for share-
holders. Many authors agree that one of the motivations for starting a
business is the creation of wealth for the owner (Barringer and Ireland
2008; Longenecker et al. 2006). These two perspectives concur, in that
one of the reasons for the existence of an enterprise is to create value
for the owner, owners or shareholders. In the case of listed companies,
their equity typically consists of publicly traded shares. The value of these
shares changes over time, depending amongst others on the market's per-
ception of the value of the company. Efficient market theory argues that
these changes are based on the fact that 'investors continuously eval-
uate all information when valuing a share' (Fama 1974; 1991). Thus, if
the market incorporates all information, measures should be derivable
that allow for the evaluation and appraisal of the intensity of a firm's
entrepreneurial orientation. Although the applicability of this rationale
has received wide acceptance, the integration of entrepreneurship and fi-
nance theory remains limited (Brophy and Shulman 1992; Vozikis et al.
1999,33).
Little previous research has focused on assessing the relationship be-
tween ei and shareholder value creation and, therefore, this study aims
to focus on this research gap. The purpose of this study is twofold. Firstly,
the ei of listed companies is determined and secondly, the relationship
between the level of ei and the shareholder value created by a company,
as represented by economic value added (eva) and the market-adjusted
total share return (tsr), is investigated. The contribution of this study
is the focus on the relationship between ei and shareholder value cre-
ation, rather than merely on the financial performance of an enterprise.
The next section provides an overview of the importance of the study,
discusses the theoretical background of key concepts and highlights the
problems of traditional versus value based financial measures. Subse-
quently the methodology, results and managerial implications are pre-
sented and discussed.
Importance of the Study
The importance of entrepreneurial behaviour in developing countries,
such as South Africa, is emphasised by intensified competition in the
global economy, the need for economic growth and the impact of en-
trepreneurial behaviour on future growth and value creation in listed
companies.
Globalisation is transforming and integrating the world's economies
(Hough 2004). Developing countries, such as South Africa, are experi-
encing the need to become more competitive and to operate globally
(Gamble and Blackwell 2002) if sustainable economic growth and de-
velopment is to occur (Porter 2004, 31). The World Economic Forum
holds that the management of technology, innovation and information
have emerged as key requirements for success in the 21st century (Claros
et al. 2006). Therefore, South African companies need to become more
entrepreneurial to increase their competitiveness, on both an organisa-
tional and country level.
Entrepreneurship is an important element in organisational develop-
ment and economic growth (Antoncic and Hisrich 2001; Drucker 2002).
Entrepreneurial behaviours and attitudes are key determinants of the
ability of established firms to survive and prosper in turbulent envi-
ronments (Lumpkin and Dess 1996). Consequently, listed companies,
state-owned enterprises and small and medium-sized businesses are be-
ing urged to be more entrepreneurial. Several authors argue that tradi-
tional management methods that focused on control and efficiency no
longer suffice in the knowledge economy where adaptability and creativ-
ity drive business success (Hamel and Breen 2007; Leibold et al. 2002).
In South Africa entrepreneurship is also seen to be vital to address the
issues of job creation, economic growth and the exploitation of oppor-
tunities (Von Broembsen, Herrington, and Wood 2005). The pursuit and
exploitation of opportunities accentuate financial objectives for the firm,
such as profitable customer acquisitions and market growth. Thus the
creation of additional value, or wealth, for the owner-entrepreneur or
for a group of owners (shareholders) is one of the main objectives of en-
trepreneurial activities. Such an emphasis makes the use of value based
performance measures particularly relevant (Vozikis et al. 1999, 34).
The potential of entrepreneurial behaviour to create shareholder value
is implicit in the growth strategies companies pursue. When evaluating
the financial performance of a company, it is important to distinguish
between the value resulting from its current activities, and the value of
future growth activities. According to O'Byrne (2000) it is not sufficient
to merely maintain the current level of financial performance in order to
maximise the shareholder value of a firm. The expected future growth in
financial performance should also be considered, since it could signifi-
cantly contribute to the total value of the firm. Madden (1999) indicates
that firms which do not continuously act in an innovative way to increase
their economic lifetime will ultimately cease to create economic value
(the return on investment drops below the return required by investors).
Such firms will face financial failure as investors move their investments
to other firms that offer them acceptable levels of return.
To summarise, the importance of this study is emphasised by the
role entrepreneurship could play in developing countries, such as South
Africa; intensified competition in the global economy and the poten-
tial of entrepreneurial actions to create value for shareholders; and the
impact of entrepreneurial behaviour on future growth and shareholder
value creation in listed companies.
Theoretical Background
entrepreneurial intensity
Although there has been intense debate on how to define entrepreneur-
ship, many authors (Morris and Kuratko 2002; Barringer and Ireland
2008) concur with Stevenson, Roberts, and Grousbeck (1989) that en-
trepreneurship can be described as 'the process of creating value by
bringing together a unique combination of resources to exploit an op-
portunity'. This definition implies that (1) entrepreneurship may vary in
terms of the extent and number of times it occurs, (2) entrepreneurship
occurs in various contexts (start-up, corporate and others); (3) that it
is a process that can be managed; and (4) that it creates value and it is
opportunity-driven.
Implicit in the definition provided by Stevenson, Roberts, and Grous-
beck (1989), entrepreneurship may vary in terms of extent and the num-
ber of times it occurs. Morris and Sexton (1998) refer to the varying levels
of entrepreneurship as Entrepreneurial Intensity (ei). They view ei as a
function of the degree and frequency of entrepreneurship as shown in
figure 1 (Morris and Sexton 1996). The notion of entrepreneurial inten-
sity is derived from the conjecture that entrepreneurial behaviour may
figure 1 Entrepreneurial Intensity (adapted from Morris and Sexton 1996)
differ in terms of its levels of innovativeness, pro-activeness and risk-
taking characteristics. This variation may be visualised as opposites on
a conceptual continuum, where one extreme would represent conser-
vative behaviour and another extreme would represent entrepreneurial
behaviour (Barringer and Ireland 2008). Frequency of entrepreneurship
refers to the number of times an enterprise acts entrepreneurially (for
example, develops new products or processes), while the degree of en-
trepreneurship (also referred to as entrepreneurial orientation) could
be assessed by three dimensions: innovativeness, risk-taking, and pro-
activeness.
Innovativeness, the first dimension of the degree of entrepreneurship,
refers to the ability to generate ideas that will culminate in the produc-
tion of new products, services and technologies. Risk-taking, the second
dimension, involves the determination and courage to make resources
available for projects that have uncertain outcomes, in other words in-
volve risk. Attempts are made to manage these risks by researching a
market, recruiting and employing skilled staff or other strategies. Pro-
activeness, the third dimension, indicates top management's stance to-
wards opportunities, encouragement of initiative, competitive aggres-
siveness and confidence in pursuing enhanced competitiveness (Morris
1998). In the view of Morris and Sexton (1996) ei is a function of the de-
gree and frequency of entrepreneurship. This is supported by Antoncic
and Hisrich (2001). Conversely, Lumpkin and Dess (1996) argue that not
three, but five dimensions should be used to measure entrepreneurship,
namely autonomy, competitive aggressiveness, pro-activeness, innova-
tiveness and risk-taking. In contrast with these views, this study argues
for a view of autonomy as an internal condition that influences the or-
ganisational climate. Competitive aggressiveness forms part of the pro-
activeness sub-dimension. Other researchers support this view (Morris
et al. 2006; Kreiser, Marino, and Weaver 2002).
The term Entrepreneurial Intensity ( ei ), therefore, refers to the variable
nature of entrepreneurship within an established enterprise. As shown in
figure 1, various positions of ei are possible, since different scores can be
obtained on the frequency axis and degree of entrepreneurship axis. Al-
though Morris and Sexton (1996) assessed entrepreneurial intensity in
a corporate context, entrepreneurship can indeed occur in various con-
texts.
These organisational contexts may range from start-up firms, grow-
ing independent businesses, multinationals, even to non-profit organ-
isations, such as semi-state institutions or organisations with a social
purpose. Within these different contexts the definition of Stevenson,
Roberts, and Grousbeck (1989) applies, since the process and required
inputs are similar, even if the outputs differ. Although authors distin-
guish between corporate entrepreneurship, intrapreneurship and en-
trepreneurship (Birkinshaw 2003; Sharma and Chrisman 1999), the sim-
ilarities between these contexts are generally greater than the differences
(Morris and Sexton 1996). Corporate Entrepreneurship (ce), generally,
refers to the development of new business ideas and opportunities within
large and established corporations (Birkenshaw 2003). In most cases, ce
describes the total process whereby established enterprises act in an in-
novative, risk-taking and pro-active manner (Zahra 1993; Dess, Lump-
kin, and McGee 1999; Bouchard 2001), where intrapreneurship is gener-
ally used to refer to the behaviour of the individual. Guth and Ginsberg
(1990) argued that ce is any effort to combine resources in new ways
in order to create value for the firm. In all the different contexts en-
trepreneurship can be seen as a process with different stages.
Even though entrepreneurship and innovation are inherently unpre-
dictable, chaotic and creating ambiguity, the entrepreneurial process can
nevertheless be managed. Entrepreneurial events are characterised by
different stages, such as opportunity identification, business concept
definition, assessment of the resource requirements, acquisition of the
needed resources, and then management and harvesting of the business
(Morris and Kuratko 2002). The ability to act entrepreneurially is linked
to the perception of opportunity.
The pursuit of opportunities also emphasises that those opportuni-
ties, which create the greatest value, should be exploited. Schumpeter
(1934) pioneered the theory of economic development and value creation
through the process of technological change and innovation. He intro-
duced the notion of'creative destruction' (Schumpeter 1942) noting that,
following a technological change, certain rents become available to entre-
preneurs, which later diminish as innovations are adopted. These rents
were later named Schumpeterian rents, defined as rents stemming from
risky initiatives and entrepreneurial insights in uncertain and complex
environments. Corporate entrepreneurship should not only focus on
seeking new markets, but also create new opportunities in existing mar-
kets (Block and MacMillan 1993). Thus the creation of value or wealth
for the owner-entrepreneur or for a group of owners (shareholder) is the
objective of entrepreneurial activities. Such an emphasis makes the use of
shareholder value creation (or destruction) particularly relevant (Vozikis
et al. 1999).
To conclude, firms may vary in terms of the intensity of their en-
trepreneurial actions. Actions could be regarded as entrepreneurial if
they are focused on opportunities and create value, regardless of the
resources that firms control. The focus of this study is on the en-
trepreneurial intensity in established firms and the value that is created
for shareholders.
shareholder value creation
Since the purpose of any enterprise is defined as long-term value cre-
ation, its corporate performance should be measured by considering
the value it created (Monks and Minnow 2001). Value Based Manage-
ment (vbm) could be defined as the process of continuously maximising
shareholder value (Copeland, Koller, and Murin 1994). When applying
vbm techniques, shareholder value creation becomes the main objective
of all employees and the management of the enterprise.
According to Copeland et al. (1994), vbm is a combination of two el-
ements. On the one hand it consists of adopting a value-creation mind-
set throughout an enterprise. All employees should understand that all
their actions should be directed towards achieving this objective. Fur-
thermore, this value-creation mindset needs to be combined with the
necessary management processes and systems to ensure that the employ-
ees actually behave in a manner that creates value (Copeland et al. 1994).
problems with traditional financial measures
During the last three decades, a number of studies empirically analysed
the relationship between ce and organisational performance (Goosen,
DeConing and Smit 2002; Kreiser, Marino and Weaver 2002; Zahra and
Garvis 2000; Lumpkin and Dess 1996; Singh 1990). Although many of
these report a positive relationship, most call for longitudinal designs
since entrepreneurial activities contribute to the long-term performance
of the enterprise (Goosen et al. 2002; Antoncic and Hisrich 2001). For ex-
ample, the cost of implementing entrepreneurial initiatives may be high
in the initial year of implementation, especially in the areas of product
and process innovations. The returns on such investments may only be
realised two to three years in the future, since radical product innova-
tions may take time to diffuse through the market. Processes and busi-
ness innovations need to be understood and used by employees to yield
economies of scale or scope (Barringer and Ireland 2008; Schilling 2008).
Zahra (1993) argues that the strength of the relationship between ce and
organisational performance will increase over time. In contrast, other
authors indicate that the existence of such a relationship depends on the
circumstances in the external environment, and the perceptions of man-
agement (Zahra, Nielsen, and Bogner 1999).
The majority of the studies investigating the relationship between ce
and financial performance utilised traditional accounting-based mea-
sures to evaluate financial performance. These traditional financial per-
formance measures predominantly focus on the short-term financial
performance of an enterprise (the measures are usually calculated for
a fiscal year). The benefits of entrepreneurial activities are, however, usu-
ally experienced over the long-term, and this casts some doubt on the
suitability of using the traditional measures to quantify financial perfor-
mance (Vozikis et al. 1999,35-36).
Furthermore, the accounting treatment of items such as research and
development (r&d) and goodwill negatively influences the short-term
financial performance of an enterprise. Although these expenses are ex-
pected to generate profits in future, the full amounts are usually allo-
cated during the financial year in which they were incurred. This may
have a negative effect on innovation, since management and divisions
may postpone or decrease expenditure and efforts on r&d to maintain
current profit levels. Value based measures aim to overcome some of the
limitations of traditional financial measures.
traditional vs. value based financial measures
The major financial objective of an enterprise is the maximisation of
shareholder value (Brigham and Houston 2001). All management deci-
sions and strategies should contribute towards this objective. Manage-
ment, however, faces the problem of determining what the effect of its ac-
tions would be on the future financial performance of the enterprise. In
order to quantify and manage financial performance, a significant num-
ber of traditional financial measures have been developed. These mea-
sures, however, are exposed to numerous weaknesses. Amongst others,
the measures are exposed to accounting distortions (Stewart 1991; Ehrbar
1998), they exclude the enterprise's cost of capital (Young and O'Byrne
2001), and they are based on historic cost information rather than cur-
rent replacement values (Peterson and Peterson 1996). In the majority of
previous studies where the relationship between ce and financial per-
formance were investigated, these traditional measures are applied as
measures of financial performance (Goosen, DeConing, and Smit 2002;
Kreiser, Marino, and Weawer 2002; Lumpkin and Dess 1996; Zahra 1993).
With a view to overcome some of the limitations associated with the
traditional measures, a number of vb financial performance measures
were developed. Proponents of the vb measures report high correlations
between the measures and the creation of shareholder value (Stewart
1994; Walbert 1994; O'Byrne 1996) and they are considered to be a ma-
jor improvement over the traditional financial performance measures.
These measures attempt to remove some of the accounting distortions
contained in the traditional measures. The most important improve-
ment, however, is that the value based measures include the enterprise's
cost of capital in their calculation. An enterprise's cost of capital is in-
fluenced by the market's perception of its risk and its expected future
returns. By incorporating the cost of capital, the value based measures
evaluate the market's perception of the current, as well as the expected
future financial performance of the enterprise.
According to Brophy and Shulman (1992), these vb measures should
more accurately reflect the financial performance of a company. In the
case of listed companies, share prices reflect the market's perception of
the risk and future return of the companies. Since their share prices are
readily available, it should be easier to quantify cost of capital figures for
listed enterprises than for smaller delisted or newer firms. They argue
that by considering these vb measures, researchers are able to evaluate
not only the current market valuation of a company, but also changes in
this valuation.
One of the most well-known and widely applied vb measures is Eco-
nomic Value Added (eva®). This measure, which was developed and
trademarked by the New York consulting firm Stern Stewart, calculates
the difference between an enterprise's Net Operating Profit after Tax
(nopat) and a capital charge (Young and O'Byrne 2001). The capital
charge is determined by multiplying the enterprise's Weighted Average
Cost of Capital (wacc) with the invested capital at the beginning of the
financial year. The eva value represents the enterprise's economic, rather
than accounting profit (Peterson and Peterson 1996), and makes provi-
sion not only for the cost of debt capital, but also the cost of all other
forms of capital (Grant 2003). Maximising an enterprise's eva should
result in an increase in the shareholder value created (Stewart 1991).
In terms of the prerequisites identified in Goosen et al. (2002), eva
offers a number of improvements over the traditional financial perfor-
mance measures. Most importantly, the implementation of a vbm sys-
tem based on eva should ensure an increased focus on the creation of
shareholder value not only at the corporate level, but also at divisional
levels (Young and O'Byrne 2001). Since eva can be calculated and in-
terpreted at the divisional level, employees are able to understand their
influence on the enterprise's overall value creating ability. Alternatively,
eva can be translated into divisional value drivers in those cases where
the calculation of the measure proves problematic, or where the value
drivers are more directly linked and controlled by the division (Young
and O'Byrne 2001). Translating eva into these value drivers and com-
bining it with EVA-based bonuses could enhance innovation (Young and
O'Byrne 2001).
entrepreneurial intensity and value creation
A significant benefit from the measures eva and tsr is that such an ap-
praisal of the firm reflects not only the company's expected financial per-
formance, but also the market's evaluation of the firm's entrepreneurial
posture. This is a reflection of a company's current actions, including
the company's pursuit of opportunities, and how such pursuits change
a company's future competitiveness when valuing a share. Additionally
the market evaluates the characteristics of a firm that may impact on the
pursuit of future opportunities, such as new product, process, service or
business developments in response to changes in the environment.
Furthermore, value based measures could provide greater insight than
accounting measures alone. For example, the economic value added and
created represents more than growth in a single accounting measure or
in the size of the company. Rather, additional value creation occurs when
the market place confers a positive judgment on the overall actions of the
firm. This is critical since it is possible for a firm to experience growth in
accounting or size variables, without the creation of additional value.
The measure of eva has the benefit of being based on the firm's out-
puts, i. e., cash flows resulting from the intensity of entrepreneurial ac-
tions (Bruton et al. 1996). Since the long-term view of the benefits of ce
are taken into consideration and the timing (and risk) of the benefits are
considered, eva and tsr do not exhibit the problems associated with the
traditional accounting-based measures.
Finally eva has practical applicability, because there is evidence that it
may provide insightful differentiation among various firms' ei positions,
since all firms are not equally entrepreneurial. The differences between
firms should be reflected in the value creation measure. Firms that cre-
ate additional value over time should have a higher ei over time, as they
have been the best at creating and pursuing opportunities in the environ-
ment. Those firms that destroy value over time should have the lowest
ei. Therefore, it could be argued that value based measures, such as eva
(internal measure) and tsr (external measure), should show a positive
relationship with increased ei.
Methodology
The objective of this study is firstly to determine the entrepreneurial in-
tensity of listed companies and secondly to investigate the relationship
between ei and eva and ei and tsr respectively. The sample and mea-
suring instruments will subsequently be discussed.
sample and data collection
Data regarding the ei of companies were collected by a cross-section tele-
phone survey between August and October 2005. The key respondent
was the relevant Chief Information Officer (cio). A total of 82 compa-
nies participated in this survey, and were included in the initial sample.
In order to calculate the eva and tsr values, the enterprises had to be
listed on the Industrial Sector of the jse Securities Exchange for the pe-
riod 2003 to 2005. Since the industrial sector is the second largest sector
in the South African economy and accounts for just over 16 percent of
the country's gdp (Mboweni 2006), this is a legitimate sample for this
type of study. Linked to the innovation imperative of established busi-
nesses and the fact that listed companies incorporate market perceptions
of their cost of capital, this sample is well suited to the type of analysis
conducted in this study. Because of the nature of their operations, enter-
prises listed in the Financial and Mining sectors were excluded from the
study. When considering the initial sample, a total of 79 enterprises pro-
vided complete eva values for the period under investigation. In order
to be included in this study, enterprises had to provide complete eva,
tsr and ei data. A total of 55 enterprises provided the necessary values,
and are thus analysed in the remainder of the study.
measuring instruments
A measurement instrument was adapted to assess ei within South
African enterprises. In order to ensure its validity and reliability, items
from existing measuring instruments that have proved to be reliable and
valid in previous research studies were used where possible, such as the
Entrepreneurial Performance Index (epi) of Morris and Sexton (1996)
and the entrescale (Kwandwalla 1977; Miller and Friesen 1978; Covin
and Slevin 1989; Knight 1997). These were enhanced by questions for-
mulated by the researchers (based on the literature) to ensure that each
variable in the measurement instrument was represented by at least three
items. Respondents needed to indicate their answers on a 9-point Likert
scale, since it is easier for a respondent to visualise a 9-point scale, as op-
posed to a 7-point scale, when participating in a telephone interview. ei
consists of the degree and frequency of entrepreneurship. Degree of en-
trepreneurship consists of three dimensions: innovativeness, risk-taking
and proactiveness.
•	Innovativeness: Three items measure the relative innovativeness of
a company: emphasis on r & d or marketing of existing products,
the number of new products and degree of change in product lines
over the last two years. Respondents were asked to indicate to what
extent their companies reflect these types of behaviour. The mean
score, calculated as the average of three items, was used to assess a
company's relative innovativeness.
•	Risk-taking: Three items assess the relative risk-taking propensity of
a company: the degree of risk (low vs. high) of projects; the strategic
posture (wait-and-see or bold and aggressive) of the company and
the type of behaviour to achieve goals (cautious vs. bold). The items
requested respondents to specify to what extent their companies re-
flect these types of characteristics. The mean score, calculated as the
average of three items, measured a company's relative risk-taking
propensity.
•	Proactiveness: Three items gauged the proactiveness dimension of
a company: posture towards competitors, initiator of action and
first-to-market or follower strategy. Respondents were required to
signify to what extent their companies reflect these types of actions.
The mean score, calculated as the average of three items, was used
to determine the relative proactiveness of a company.
Frequency, which refers to the number of entrepreneurial events,
may be applied to many different areas, including the introduction
of new products, services, processes, as well as new businesses. The
Entrepreneurial Performance Instrument (epi) questionnaire, used by
Morris and Sexton (1996), contained a number of items to measure fre-
quency. These items were related to new product, new service and new
process introductions. Since this study viewed new business develop-
ment as a part of ce, the questionnaire was expanded to include this
dimension as well.
•	Product frequency: Respondents were informed that new product
introductions refer to repositioning of products, product improve-
ments, and additions to product lines, new category entries as well
as new-to-the-world products. They were requested to rank the de-
gree of product improvements over the past two years, compared
to the past five years relative to their own performance and the per-
formance of their competitors on a 9 point Likert-scale with 1 be-
ing significantly less and 9 being significantly more. They were also
asked to indicate the degree of change in their products (improve-
ments or 'new-to-the-world' products). The mean of three items
provided an indication of product frequency.
•	Service frequency: Service introductions include modifications of
existing services, additions and services not offered before, and re-
spondents were asked to rate the degree of service improvements
over the last two years compared to the past five years relative to
their own performance and that of competitors; as well as the de-
gree of change in service offerings (improvements or services that
did not previously exist in the market) on a 9-point Likert scale.
Service frequency was assessed by the mean of these three items.
•	Process frequency: Process innovations refer to new systems for man-
aging inventories, an improved process for collecting outstanding
debtors or other processes that could improve the effectiveness or
efficiency of operations. Respondents were required to appraise the
degree of process improvements over the last two years compared
table 1 A summary of Cronbach Alpha coefficient values to determine the internal
consistency of Entrepreneurial Intensity
Construct	Cronbach Alpha Coefficient Values
Degree of Entrepreneurship	0.88
Innovativeness	0.80
Risk-taking	0.88
Proactiveness	0.77
Frequency	0.79
Frequency Product	0.68
Frequency Service	0.74
Frequency Process	0.77
Frequency Business	0.67
to the past five years relative to their own performance, that of
their competitors, and also to what extent these processes were new
(improvements or processes not previously used in industry). The
mean score of these three items provided an indication of the pro-
cess frequency of a company's behaviour.
• Business development frequency: New business was seen as new mar-
kets, acquisitions and mergers, internal ventures and spin-offs. Re-
spondents were asked to evaluate the degree of business develop-
ment over the past two years, compared to the past five years rel-
ative to the company's own performance and that of competitors.
Additionally the degree of new business development (market pen-
etration or market development) was also assessed. Business devel-
opment was measured by the mean of these three items.
Cronbach Alpha coefficients were computed to assess the internal
consistency of the constructs assessed by the measurement instrument.
These values are shown in table 1. The two constructs frequency and de-
gree ofentrepreneurship Cronbach Alpha coefficients were 0.79 and 0.88
respectively. The dimensions of degree of entrepreneurship innovative-
ness, risk-taking and pro-activeness were 0.80,0.88 and 0.77 respectively.
The dimensions of frequency product, service, process and business were
0.68, 0.74, 0.77 and 0.67 respectively. These coefficients would appear to
satisfy Nunally's (1978) suggested minimum criteria for internal reliabil-
ity. Coefficients lower than 0.5 are regarded as questionable, coefficients
close to 0.70 as acceptable and coefficients of 0.80 as good (Sekaran 1992).
The eva figures used in this study were obtained from the McGregor
bfa Database (2005). The eva values for the most current financial year
(eva2005) were downloaded. In order to investigate the longitudinal na-
ture of the relationship between ei and eva, the change in eva over the
period 2003 to 2005 (eva2003-2005) was also calculated. Since the absolute
monetary values of eva are influenced by the size of the enterprise, the
eva values were standardised by dividing the figures by the enterprise's
invested capital at the beginning of the financial year (ict-1).
eva = nopat - (wacc X ICt-1).	(1)
Dividing throughout with ict-1 yields:
eva nopat wacc X ict_ 1
- = - = - = roce - wacc,	(2)
Ict-1 Ict-1 Ict-1
where: roce = Return on Capital Employed and wacc = Weighted Av-
erage Cost of Capital.
The resulting figures provide an indication of the percentage margin
earned above (or below) the enterprise's wacc. Positive values indicate
excess returns, while negative values indicate returns below the wacc.
The market-adjusted share return (tsr) was calculated as the differ-
ence between the annual compounded return on a company's shares, and
that of the All Share Index (alsi). This value represents the excess return
earned on the share above (or below) the overall market return. In order
to calculate the annual compounded share return, the monthly returns
on the share (consisting of the monthly capital gain/loss and all divi-
dends received during the month) were calculated first. A twelve-month
period ending December 2005 was used to calculate the compounded an-
nual return on a share. Similarly, the monthly returns on the alsi index
were calculated (including dividend payments), and compounded over
the corresponding period. Both the share and alsi compounded returns
were obtained from the McGregor bfa database (2005).
The statistical analysis was conducted using Statistica version 7.1. Cor-
relations and best subset regression analyses were used to determine the
relationship between ei, its constructs and eva and tsr.
Results
The first part of this section describes the sample by focusing on the size
of companies (as measured by the number of employees) and also pro-
vides descriptive statistics of entrepreneurial intensity and its constructs.
>3000
1000-2999
500-999
200-499
1-199
15%
15%
24%
figure 2 Size of companies in the sample, reflected by the number of employees
(n = 72)
table 2 Descriptive statistics of degree and frequency of entrepreneurship
and its dimensions
Variables	Mean	Median	Std. dev.	Coef. var.	N
Degree of entrepreneurship	5.4634	5.4444	1.1023	0.2018	82
Innovativeness	5.4715	5.3333	1.5126	0.27648	82
Pro-activeness	6.1056	6.3333	1.5404	0.2523	82
Risk-taking	4.8130	4.667	1-738	0.3611	82
Frequency of entrepreneurship	5.8894	5.9167	1.3308	0.2260	82
Product	5.8694	6.0000	1.5168	0.2584	82
Service	5.7843	5.6667	1.7561	0.3036	82
Process	5.6573	6.0000	1.8179	0.3213	82
Business	5.8148	5-8333	1.6358	0.2813	82
Entrepreneurial Intensity	11.4469	11.2222	2.0206	0.1765	82
19%
The second part contains the results from the correlation analyses, while
the final part presents the results of the best subset regression.
The sample of firms used in the study consists of listed companies on
the Industrial Sector of the jse. As shown in figure 2 most of the com-
panies (27%) employed 3000 and more employees, while 24% employed
less than 200 employees. Thus both smaller and larger companies formed
part of the sample.
Table 2 contains descriptive statistics of entrepreneurial intensity and
its dimensions, using the mean, median, standard deviation and coeffi-
cient of variation to describe the data.
Table 2 describes the Entrepreneurial Intensity of companies listed in
the Industrial Sector of the jse. Examining the dimensions of degree of
entrepreneurship, it would be seen that most companies score the highest
on the pro-activeness dimension with a mean of 6.11, while their risk-
taking propensity is the lowest with a mean of 4.81. The frequency of
table 3 Correlation coefficients between eva2005 and eva2003-2005
and the independent variables (p values in parentheses)
Correlations	EVA2005	eva2003-2005
Entrepreneurial Intensity	0.0841 (0.5341)	0.2823** (0.0368)
Degree of entrepreneurship	0.1474 (0.1919)	0.3057*** (0.0065)
Innovativeness	0.0882 (0.4364)	0.1532 (0.1805)
Risk taking	0.1189 (0.2935)	0.1857 (0.1036)
Pro-activeness	0.0962 (0.3962)	0.3032*** (0.0070)
Frequency	0.0637 (0.6378)	0.1598 (0.2439)
Product	0.1904 (0.1091)	0.1716 (0.1554)
Service	0.1313 (0.2895)	0.2624** (0.0347)
Process	0.0540 (0.6592)	0.1344 (0.2780)
Business	-0.0015 (0.9903)	-0.1603 (0.1916)
notes *** Significant at the 1 percent level. ** Significant at the 5 percent level.
entrepreneurship overall has a higher mean of 5.89 compared to degree of
entrepreneurship (mean = 5.46). Product frequency innovations are the
highest (mean = 5.87), while process innovations are the lowest (mean =
5.66).
The next section contains an analysis of the relationship between ei
and eva and between ei and tsr by employing correlation analysis.
Thereafter a best subset regression analysis is used to determine which
of the dimensions of degree and frequency of entrepreneurship explains
the majority of the variance in eva and tsr.
Correlation Analysis
entrepreneurial intensity and economic value added
Perusal of table 3 indicates that the correlations between eva2005 and the
independent variables are all statistically insignificant. The correlation
coefficients are all low, and indicate that no statistically significant rela-
tionships exist between eva2005 and any of the independent variables.
The value of eva2005, however, is calculated by considering only the
most recent financial information. When investigating the relationship
between ce and financial performance a longitudinal approach is pre-
ferred (Goosen, DeCining, and Smit 2002). To address this approach, the
change in an enterprise's level of eva from 2003 to 2005 (eva2003_2005)
was also calculated. When the correlations between this measure and the
independent variables were considered, some were found to be statisti-
cally significant.
The correlation between ei and eva2003-2005 is statistically significant
(p < 0.05). It appears that as the degree of entrepreneurship increases,
there is also an increase in the economic value added. This finding is sup-
ported by the literature, which argues that higher levels of entrepreneur-
ship should lead to value creation by the enterprise over the longer term
(Hayton 2005; Goosen, DeConing, and Smit 2002; Zahra, Nielsen, and
Bogner 1999).
In general, the degree of entrepreneurship exhibits a statistically sig-
nificant relationship with eva2003-2005 (p < 0.01). The components of
the degree of entrepreneurship, innovativeness, pro-activeness and risk-
taking, exhibit varying levels of significance. Although numerous au-
thors (Barringer and Bluedorn 1999; Birkinshaw 2003; Dess et al. 1999)
advocate the importance of innovation in today's complex business en-
vironment, it appears that there is no statistically significant correlation
between innovativeness and eva2003-2005 in the case of this dataset. In
addition, no statistically significant correlation was found between risk-
taking and eva2003-2005. The companies used for this analysis are listed
companies and are accountable to shareholders. It is therefore, reason-
able to expect that they would be cautious and careful in managing risk
regarding uncertain investments. However, a statistically significant cor-
relation exists between pro-activeness and eva2003-2005 (p < 0.01). Pro-
activeness reflects the tendency of top management to anticipate future
trends, opportunities and initiate strategies, rather than follow competi-
tors. This type of commitment is essential for vbm systems to be suc-
cessfully implemented in companies.
No statistically significant correlation was found between frequency
of entrepreneurship and eva2003-2005. In terms of its dimensions: prod-
uct, process or business frequency, no statistically significant correlation
was found. However, frequency of service innovations shows a statisti-
cally significant correlation with eva2003-2005 (p < 0.05). This is a sur-
prising finding, but may be ascribed to service innovations generally be-
ing less costly to implement than product, process and business innova-
tions.
entrepreneurial intensity and total share return
Examination of table 4 indicates that the correlation between ei and
tsr is not statistically significant. A statistically significant correlation,
Entrepreneurial Intensity and Shareholder Value Creation 247
table 4 Correlation coefficients between tsr and the independent variables
Variables
TSR
Entrepreneurial Intensity
Degree of entrepreneurship
Innovativeness
Risk taking
Pro-activeness
Frequency
0.2663**
0.2217**
0.1460
0.0721
0.1262
0.0345
Product
Service
Process
Business
0.0073
0.2050*
0.2179*
0.0193
notes ** Significant at the 5 percent level. * Significant at the 10 percent level.
however, exists between the degree of entrepreneurship and tsr. One
should bear in mind that the tsr measure used in these analyses rep-
resents excess return earned on the share above the overall market re-
turn. Therefore, it would appear that as the degree of entrepreneurship
increases, the shareholder value increases in excess of the market return.
Even though the tsr values are calculated for the current year only, these
values should reflect market perceptions with regard to future financial
performance in an efficient market (Biddle, Bowen, and Wallace 1997).
The literature argues that the strength of this value creation will increase
over time as levels of entrepreneurship increase (Hayton 2005; Goosen,
DeConing, and Smit 2002; Zahra, Nielsen, and Bogner 1999).
Similarly to the results obtained for eva2003-2005, the components
of the degree of entrepreneurship show varying significance levels. No
statistically significant correlation between innovativeness and tsr and
risk-taking and tsr was found. However, a statistically significant corre-
lation exists between pro-activeness and tsr (p < 0.05). Pro-activeness
reflects the tendency of top management to spot trends and opportuni-
ties to take initiative in the market, rather than being reactive and fol-
lowing competitors. This finding also implies that a pro-active attitude
of top management generates excess shareholder returns.
Comparable to the results of ei and eva2003-2005 no statistically signif-
icant correlation was found between frequency of entrepreneurship and
TSR. In terms of its dimensions: product or service frequency, no statis-
tically significant correlation was found. However, frequency of process
and business innovations show a statistically significant negative corre-
lation with tsr (p < 0.10). This finding is to be expected, and may be
ascribed to the cost of process and business innovations often being high
in the year they are implemented, while the returns realised from these
innovations are generally evident over a longer term.
Additionally it should be pointed out that several external market fac-
tors also influence tsr. Examples of these include general economic con-
ditions, irrational market behaviour, and global market crises. Since ei
only partly contributes to the overall tsr value, it is unrealistic to expect
very high correlations with tsr.
Regression Analysis
Multiple regression analysis was used to assess the degree of relation-
ship between the vb measures and the dimensions of degree and fre-
quency of entrepreneurship. The first regression model assessed was
the relationship between degree and frequency of entrepreneurship and
the change in eva (eva2003-2005). These results indicated that although
the degree of entrepreneurship had a statistically significant relationship
with eva2003-2005, only 7.62% (adjusted R2 = 0.0762) of the variance in
eva2003-2005 was explained by degree of entrepreneurship. The second
regression model assessed the relationship between ei and tsr, to deter-
mine whether higher levels of entrepreneurship resulted in excess share
returns. Again, the results indicated that degree of entrepreneurship had
a significant relationship with tsr. The adjusted regression coefficient,
however, was only 0.0938. Therefore, it was decided to use best subset
regression analysis to determine how the separate dimensions of degree
and frequency of entrepreneurship influence these two vb measures.
Best subset regression analysis runs all possible regressions between
the dependent variable and all possible subsets of independent variables
and enables the user to find the best regression model, given a specified
number of independent variables (fewer than 14). It excludes variables
which do not contribute to increasing the regression coefficient. The cri-
terion used in determining which estimated regression equations are the
best for only a number of predictors is the value of the coefficient of de-
termination (R2) (Hair et al. 2006). Consequently, best subset regression
analysis has the benefit over stepwise regression, forward selection and
backward elimination for which the best model for a given number of
variables will be found.
table 5 Summary of the best subset regression analysis conducted to determine the
most relevant dimensions of ei for eva2oo3—2005
Variables	ß	Std. err. ofß	B	t( 50)	p-level
Intercept	-	-	-16.7512	-1.68	0.09*
Pro-activeness	0.2905	0.1317	2.9436	2.21	0.03**
Risk taking	0.1894	0.1256	1.6972	1.51	0.13
Product	-0.3938	0.2222	-4.2504	-1.77	0.08*
Service	0.7415	0.2092	6.9883	3.54	0.00**
Business	-0.3769	0.1434	-3.6043	-2.63	0.01**
Innovativeness	-	-	-	-	-
Process	-	-	-	-	-
notes R = 0.5657, R2= 0.3200, adjusted R2 = 0.2520, F(5,50) = 4.7051, p < 0.0013; std.
err. of estimate: 13.3818; ** significant at the 5 percent level, * significant at the 10 percent
level.
The results of the best subset regression analysis in table 5 indicate
that pro-activeness, service, business and product innovations are statis-
tically significant contributors to the variance in eva2003-2005 at the 90%
confidence level. Pro-activeness (t = 2.21) and service innovations (t =
3.54) are positively and significantly related to eva2003-2005, while prod-
uct and business innovations are negatively and significantly related to
the change in eva2003-2005. The independent variables explain 25% (ad-
justed R2 = 0.25) of the variation in eva2003-2005.
It is interesting to note that especially service innovations and the
pro-activeness dimension of the degree of entrepreneurship contribute
towards this relationship to the change in eva. Product and business
innovations show a negative relationship towards the change in eva,
since these innovations are often costly and the results seem to indi-
cate that the return on these innovations may be longer than the three
year period used in the analysis above. These results suggest that en-
trepreneurial strategies could yield long-term benefits. However, if firms
are only focused on short-term annual financial results their long-term
entrepreneurial intensity and competitiveness may decline.
A best subset regression analysis was also conducted with tsr as de-
pendent variable, but weak results were obtained. It seems that tsr,
which is an external measure of shareholder value creation, is influenced
by many external factors, such as general economic conditions, irra-
tional market behaviour, political instability of emerging markets, and
global market crises. Thus the influence of ei on the overall tsr value is
weak.
Conclusions
This study contributes to the current literature by describing the ei of
manufacturing firms listed on the jse and focusing on the relationship
between ei and shareholder value creation, rather than merely on the
accounting-based financial performance measures of an enterprise. Data
obtained from companies listed on the jse for the period 2003-2005
strongly suggest that the relationship between ei and financial perfor-
mance should be viewed longitudinally. The results indicate that there
is a statistically significant relationship between ei and the change in an
enterprise's level of eva from 2003 to 2005. It appears that companies
with higher degrees of entrepreneurship create more economic value
added over the longer term. In particular, the pro-active dimension of
the degree of entrepreneurship appears to contribute toward this value
creation. Similar results were found when the association between ei and
tsr was examined. No statistically significant relationship exists between
ei and tsr, but a statistically significant relationship exists between de-
gree of entrepreneurship and tsr, again with the pro-activeness dimen-
sion contributing to this relationship with shareholder value creation. It
appears that companies with higher degrees of entrepreneurship create
excess returns for shareholders above market returns.
Frequency of entrepreneurship (in general) was not found to exert a
statistically significant relationship with eva and tsr. The correlation
analysis indicates that frequency of product innovations shows no rela-
tionship with eva or tsr. Frequency of service innovations, however,
shows a statistically significant correlation with eva2003-2005 (p < 0.05).
This may be ascribed to service innovations generally being less costly
than product, process and business innovations. Frequency of process
and business innovations has a statistically significant negative correla-
tion with tsr (p < 0.10). This finding is to be expected, since process and
business innovations are generally more costly and take a longer period
to realise returns.
The best subset regression analysis supports the above findings and
indicates that ei explains 25% of the variance in the change in eva. The
constructs: pro-activeness, service, product and business showed the best
regression equation with the change in eva. Pro-activeness and service
innovations exhibit a positive relationship with the change in eva, which
indicates that the market evaluates top management stance towards op-
portunities, encouragement of initiative, and competitive aggressiveness
positively, as well as service innovations. However, the negative relation-
ship observed for product and business innovations suggest that the cost
of entrepreneurial initiatives is high and may in some instances only be
recouped over a longer period (more than three years).
Only companies listed on the jse could be included in this study, since
published financial and share data are not available for unlisted compa-
nies. Consequently it would prove difficult to calculate eva, cost of cap-
ital and tsr values for unlisted companies over a period of time. The
specific focus on only those companies that provided complete eva and
tsr data over the period investigated, however, could expose the study
to a survivorship bias.
Future researchers should measure ei longitudinally and ought to de-
termine whether it is a stable characteristic of a company or whether it
varies over time. These measures could then be correlated with vb mea-
sures. Whereas this study investigated the relationship between ei and
historic eva and tsr values, investigating the effect of current ei lev-
els on future eva and tsr values could provide further insight. Addi-
tional future research could focus on determining whether enterprises
that incorporate vb measures in their compensation systems may differ
in terms of ei from those that do not.
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The Relationships Among Leadership Styles,
Entrepreneurial Orientation,
and Business Performance
Chung-Wen Yang
This study aims to contribute to the knowledge of leadership styles
and entrepreneurial orientation at small and medium enterprises as
well as their effects on business performance. This study examines how
leadership style can affect the development and implementation of en-
trepreneurial orientation in small and medium enterprises in Taiwan.
It is also designed to examine the effects of leadership styles and en-
trepreneurial orientation on business performance. Significant conclu-
sions from this study are that different leadership styles may affect
business performance; that transformational leadership is significantly
more correlated to the business performance than is transactional lead-
ership and passive-avoidant leadership; that entrepreneurial orienta-
tion is positively related to performance; and that transformational
leadership with higher entrepreneurial orientation can contribute to
higher business performance.
Key Words: leadership styles, entrepreneurial orientation,
business performance
jel Classification: M10
Introduction
An effective leader influences followers in a desired manner to achieve
desired goals. Different leadership styles may affect organizational ef-
fectiveness or performance (Nahavandi 2002); Entrepreneurs have be-
come the heroes of economic development and contemporary enter-
prises (Sathe 2003). Entrepreneurial orientation is a commonly used
measure in the literature (Morris and Kuratko 2002). This concept is the
presence of organizational-level entrepreneurship (Wiklund and Shep-
herd 2005).
Some researchers have tried to combine the two concepts into en-
trepreneurial leadership to explore both leadership and entrepreneur-
ship behavior (Gupta et al. 2004; Tarabishy et al. 2005). They have tried
Dr Chung-Wen Yang is an Assistant Professor at the Department
of Business Administration, Cheng Shiu University, Taiwan.
to combine entrepreneurship with leadership into a new form of lead-
ership called entrepreneurial leadership. This new leadership model has
been used to show both entrepreneurship and leadership behavior (Tara-
bishy et al. 2005). In the dynamic, complex, and uncertain competitive
environment, a type of entrepreneurial leader who is distinct from the
behavioral form of leaders is needed (Cohen 2004).
This study was designed to examine how leadership styles can affect
the development and implementation of entrepreneurial orientation in
Small and medium enterprises (smes) in Taiwan. smes exert a strong
influence on the economies of Taiwan. It also examines the effects of en-
trepreneurial orientation and leadership styles on business performance.
The findings could contribute new knowledge in the fields of leadership
and entrepreneurship, especially entrepreneurial leadership
Literture Review and Hypothesis Development
leadership styles and business performance
Leadership style is the 'relatively consistent pattern of behavior that char-
acterizes a leader' (DuBrin 2001, 121). Today's organizations need ef-
fective leaders who understand the complexities of the rapidly chang-
ing global environment. Different leadership styles may affect organiza-
tional effectiveness or performance (Nahavandi 2002). This study focuses
on three different leadership styles: transformational leadership, trans-
actional leadership, and passive-avoidant leadership measured through
the Multifactor Leadership Questionnaire (mlq; Bass and Avolio 1995).
The concepts of transformational and transactional leadership have been
considerable interest in the leadership literature over the past several
years (Avolio and Bass 2004).
Transformational and transactional leadership are not viewed as op-
posing leadership styles. Leaders can be both transformational and trans-
actional (Lowe et al. 1996). In general, transformational leadership is
more effective than transactional leadership (Gardner and Stough 2002).
Some researchers have found data supporting the conclusion that trans-
formational leadership is superior to transactional leadership (Bass et al.
2003; Dvir et al. 2002). Transformational leadership is more strongly cor-
related than transactional leadership with higher productivity and per-
formance (Lowe et al. 1996), higher level of organizational culture (Block
2003), and higher level of emotional intelligence (Gardner and Stough
2002). Therefore, the researcher hypothesizes:
H1 Can the top-level managers' leadership styles (transformational lead-
ership, transactional leadership, and passive-avoidant leadership)
predict higher business performance?
entrepreneurial orientation and business
performance
Entrepreneurial orientation is a commonly used measure in entreprene-
urship literature. Entrepreneurial orientation is the presence of organiz-
ational-level entrepreneurship (Wiklund and Shepherd 2005). Several
researchers have agreed that entrepreneurial orientation could be ex-
plained by innovation, proactiveness, and risk taking (Wiklund 1999).
A large proportion of entrepreneurship studies assume entrepreneurial
orientation to be a unitary concept (Covin and Slevin 1989; Dess et al.
1997; Wiklund 1999). The notion of a single factor entrepreneurial orien-
tation concept also has been examined in some studies. Because the three
dimensions of entrepreneurial orientation can vary independently of one
another (Krauss et al. 2005; Kreiser et al. 2002; Lumpkin and Dess 1996;
Lyon et al. 2000; Venkatraman 1989), this study included a comparison of
the dimensions of entrepreneurial orientation and total entrepreneurial
orientation with other variables.
The assumption of entrepreneurial orientation is that entrepreneurial
businesses differ from other types of businesses. Successful corporate
entrepreneurship must have an entrepreneurial orientation (Covin and
Slevin 1989; Wiklund 1999; Wiklund and Shepherd 2003). This study
compared three dimensions of the entrepreneurial orientation and the
total entrepreneurial orientation with business performance. Hence, the
researcher hypothesizes:
H2 Can the entrepreneurial orientation (innovation, proactiveness, and
risk-taking) of smes in Taiwan predict the business performance?
leadership styles and entrepreneurial orientation
The study tried to combine these two concepts: leadership and en-
trepreneurship. The aim was to examine how leadership can affect
the development and implementation of entrepreneurial orientation in
smes in Taiwan. Entrepreneurial leadership is an effective and needed
leadership style (Cohen 2004; Fernald, Solomon, and Tarabishy 2005;
Tarabishy et al. 2005). Entrepreneurial leadership was coined by those
who realized that a change in leadership style was necessary. Entreprene-
urial leaders play a critical role in the success of new business ven-
tures. Wah (2004) suggested that future leadership research use more
quantitative approaches to survey Chinese entrepreneurial leaders. En-
trepreneurial leadership is understandable because of the uncharted and
unprecedented territory that lies ahead for businesses in today's dy-
namic markets (Tarabishy et al. 2005). Thornberry (2006) stated that
'entrepreneurial leadership is more like transformational leadership than
it is like transactional leadership, yet it differs in some fundamental ways'
(p. 24). The study also examines the effects of entrepreneurial orienta-
tion and leadership styles on business performance. Given this view the
researcher hypothesizes:
H3 Can the top-level managers' leadership styles (transformational lead-
ership, transactional leadership, and passive-avoidant leadership)
and entrepreneurial orientation of smes (innovation, proactiveness,
and risk-taking) predict higher business performance?
Methodology
research design
This study used a quantitative research method to examine the relation-
ship among leadership styles, the entrepreneurial orientation, and busi-
ness performance of smes in Taiwan; a sample of top-level managers
from smes in Taiwan was used. smes represent a major part of most
modern economies. According to the White Paper on Small and Medium
Enterprises in Taiwan (Ministry of Economic Affairs 2006), smes ac-
count for 97.8% of all businesses in Taiwan. This study focused on smes
to control for organizational size. The population for the study was based
on the cd-rom version of the 2006 Directories of Corporations (China
Credit Information Service 2006). The ccis is the leading business in-
formation agency in Taiwan, and its 2006 directories provide basic back-
ground information for 20,302 enterprises in Taiwan. Surveys were ad-
dressed to top-level managers, who were identified as the ceos, owners,
founders, managers, presidents, or heads of smes. Top-level managers
were targeted because they are the most informed about the business'
overall operational activities.
instrumentation
Three survey instruments were used. The first was the Multifactor Lead-
ership Questionnaire (mlq), which was used to measure top-level man-
agers' leadership style (transformational, transactional, and passive-
avoidant). The mlq is one of the most widely used instruments to mea-
sure transformational and transactional leadership behaviors (Avolio
and Bass 2004). The mlq Leader 5x short form consists of 45 items that
measure aspects of transformational leadership (attributed charisma,
idealized influence, inspirational motivation, individualized stimulation,
and intellectual consideration), transactional leadership (contingent re-
ward and management-by-exception: active), and passive-avoidant lead-
ership (management-by-exception: passive and laisser-faire leadership)
on a 5-point scale.
The second assessment tool was the Entrepreneurial Orientation
Questionnaire (eoq), the most widely utilized instrument for measur-
ing entrepreneurial orientation. It was developed by Covin and Slevin
(1989), based on the earlier study of Khandwalla (1976/1977) and Miller
and Friesen (1982). The eoq, which contains nine items and uses a 7-
point scale, measures three dimensions of entrepreneurial orientation
(innovation, proactiveness, and risk-taking). It is used to assess three
components of entrepreneurial orientation, with three items measuring
innovation, three items measuring proactiveness, and three items mea-
suring risk-taking.
The third assessment tool was a business performance scale devel-
oped by the researcher according to the suggestions of previous stud-
ies. Business performance is a multidimensional construct (Wiklund and
Shepherd 2005). Previous studies have often used self-reports to gather
business performance data, and these results have proven to be reliable
(Knight 2000). Wiklund (1999) suggested that performance measures
should include both growth and financial performance. Furthermore,
public information is unreliable because most smes are privately held
and have no legal obligation to disclose information. Respondents may
be reluctant to provide actual financial data (Tse et al. 2004). Hence, this
study used subjective, self-reported measures of business performance
including growth and financial performance. The business performance
scale was developed by the research according to suggestions of previ-
ous studies. The scale contains eight items and uses a 7-point Likert-
type scale. Four indicators of growth were utilized: sales growth, employ-
ment growth, sales growth compared to competitors, and market share
growth compared to competitors. The three financial performance indi-
cators were gross profit, return on assets (roa), and return on invest-
ment (roi). In addition, the researcher used an indicator of'overall per-
formance/success' to business performance (Lumpkin and Dess 1996).
sampling
A sample should be large enough to provide a credible result. Gay and
Airasian (2003) said that, when the population size is about 5,000 or
more, a sample size of 400 should be adequate (p. 113). Furthermore,
because the similar studies in Taiwan reported a low response rate for
mailed surveys, the researcher randomly selected 3,000 top-level man-
agers of smes in Taiwan through the 2006 Directories of Corporations
(cd version; China Credit Information Service 2006). Questionnaires
were delivered to 3,000 top-level managers via post service. Of these, 449
questionnaires were returned, but 26 surveys were not usable because
they were incomplete.
The 423 usable surveys were examined for accuracy of data entry,
non-response bias, missing values, reliability, and validity. None of the
nine variables - transformational leadership, transactional leadership,
passive-avoidant leadership, outcomes of leadership, innovation, proac-
tiveness, risk-taking, total entrepreneurial orientation, and total business
performance - violated the assumption of normality. Finally, 17 surveys
were deleted due to outliers, so 406 surveys without missing data re-
mained for analysis.
Data Analysis
The Statistical Package for the Social Sciences (spss 13.0) computer pro-
gram for Windows was used to conduct the statistical analysis of all data
in this study. The Cronbach' alpha coefficients of the mlq ranged from
.79 to .90. The Cronbach' alpha coefficients of the eoq ranged from .78 to
.84; the overall Cronbach' alpha coefficient of the eoq was .87. The Cron-
bach' alpha coefficient of the business performance was .91. All the co-
efficients were greater than .70, exceeding the recommended minimum
level of .7 (Nunnally 1978). This ensured that these three scales had a
very satisfactory degree of reliability. The results concerning evidence of
reliability were consistent with previous studies involving two of the in-
struments used in this study: the Multifactor Leadership Questionnaire
(mlq; Bass and Avolio 1995) and the Entrepreneurial Orientation Ques-
tionnaire (eoq; Covin and Slevin 1989). The business performance scale
also showed high reliability and inter-item correlations.
leadership styles and business performance
A Pearson product-moment correlation coefficient was used to exam-
ine the relationship between the leadership styles and the business per-
table 1 Intercorrelations for leadership styles and business performance (N = 418)
Measure	TF	TA	PA
Business Performance	.25***	■13**	-.10*
notes TF = transformational leadership, TA = transactional leadership, PA = passive-
avoidant leadership; * p < .05, ** p < .01. *** p < .001.
formance of smes in Taiwan. Transformational leadership was signif-
icantly positively correlated with total business performance (r = .25,
p < .001). There was a small, positive correlation between transforma-
tional leadership and business performance. Transactional leadership
was significantly positively correlated with total business performance
(r = .13, p = .009). There was a small, positive correlation with total busi-
ness performance (r = .13, p = .009). There was also a small, positive
correlation between transactional leadership and business performance.
Passive-avoidant leadership was significantly negatively correlated with
business performance (r = -.10, p = .036). There was a small, negative cor-
relation between passive-avoidant leadership and business performance.
As shown in table 1, transformational leadership had a stronger relation-
ship with business performance than did transactional leadership, both
with small and positive correlations. Passive-avoidant leadership had a
very small, negative correlation.
leadership styles to business performance
A standard multiple regression was performed with business perfor-
mance as the dependent variable, and scores on the mlq (transforma-
tional leadership, transactional leadership, and passive-avoidant leader-
ship) as independent variables. An analysis for evaluation of assump-
tions was performed to reduce the number of outliers and improve the
normality, linearity, and homoscedasticity of residuals. With the use of p
< .001 criterion for Mahalanobis distance, no multivariate outliers were
found in the sample (N = 406). No cases had missing data and no sup-
pressor variables were found. A residual analysis was conducted to check
assumptions. To check the scatterplot of the standardized residuals and
the normal probability plot, assumptions about residuals were met.
Table 2 shows the correlations between the variables, unstandardized
regression coefficients (B), and the standardized regression coefficients
(ß). This regression model was significantly different from zero, F (3,402)
= 9.85 and p < .001. The regression coefficient of transformational lead-
ership was different from zero, and 95% confidence limits were .28 to .79.
table 2 Regression analysis summary for the top-level managers' leadership styles
predicting business performance
Variable	B	SEB	ß
Transformational leadership	.53	.13	.26***
Transactional leadership	-. 06	.13	-.03
Passive-avoidant leadership	-.14	.08	-.09
notes R2 = .07, adjustedR2 = .06 (N = 406, p < .001), ***p < .001.
Only one independent variable contributed significantly to prediction of
business performance, transformational leadership: p < .001, sr2 = .04.
Altogether, 6% (adjusted R2) of the variability in business perfor-
mance was predicted by mlq scores on these three independent vari-
ables. Transactional leadership and passive-avoidant leadership were not
good predictors of the business performance. The transformational lead-
ership of top-level managers contributed the most to the business perfor-
mance of smes.
leadership styles to business performance:
discriminant function analysis
The researcher divided business performance into two categories by its
mean (M = 4.32): higher business performance and lower business per-
formance. A direct discriminant function analysis was performed using
three mlq variables (transformational leadership, transactional leader-
ship, and passive-avoidant leadership) as predictors of membership in
the two groups (higher business performance and lower business perfor-
mance). No cases were identified as multivariate outliers with p < .001.
An analysis for evaluation of assumptions of linearity, normality, multi-
collinearity, and homogeneity of variance-covariance matrices did not
violate multivariate analysis.
A discriminant function analysis indicated a strong association be-
tween groups and predictors, x2 (3) = 14.91 and p = .002. Table 3 presents
the correlation of predictor variables with discriminant functions, which
suggested that the good predictors for distinguishing between higher
business performance and lower business performance were transforma-
tional leadership and transactional leadership.
smes with higher business performance had higher scores on trans-
formational leadership (M = 3.10, SD = .45) than on transactional lead-
ership (M = 2.72, SD = .49) or passive-avoidant leadership (M = 1.33, SD
table 3 Correlation of predictor variables from the mlq with discriminant functions
(function structure matrix) and standardized discriminant function
coefficients
Predictor variable	Correlation with discriminant functions	Standardized discriminant function coefficients
	Function 1	Function 1
Transformational	.96	1.10
Transactional	. 46	-.23
Passive-avoidant	-.27	-.20
table 4 Means and standard deviations of predictor variables from the mlq as a function of business performance (N = 406)		
Predictor variable	Lower business perf.	Higher business perf.
	M SD	M SD
Transformational Leadership	2.91 .51	3.10 .45
Transactional Leadership	2.63 .52	2.72 .49
Passive-avoidant Leadership	1.40 .63	1.33 .62
= .62). smes with lower business performance also had higher scores on
transformational leadership (M = 2.91, SD = .51) than on transactional
leadership (M = 2.63, SD = .52) or passive-avoidant leadership (M = 1.40,
SD = .63). Table 4 presents the results.
Higher business performance differs from lower business performance
on transformational leadership, F (1,404) = 14.01 and p < .001. The group
of higher business performance did not differ from the group of lower
business performance on transactional leadership, F (1,404) = 3.24 and p
= .07, or passive-avoidant leadership, F (1,404) = 1.14 and p = .29. Among
the three different leadership styles, transformational leadership (Wilks'
A = .96) was the best predictor of higher business performance. Table 5
presents the predictor variables.
table 5 Predictor variables from the mlq in stepwise discriminant function analysis
Predictor variable	Wilks' A	Equivalent F (1,404)
Transformational leadership	.96	14.01***
Transactional leadership	.99	3.24
Passive-avoidant leadership	•99	1.14
notes * p <.05,*** p <.001.	Volume 6 •	Number 3 • Fall 2008
table 6 Classification analysis for business performance
Actual group membership
N
Lower Business Performance	200
Higher Business Performance 206
Predicted group membership
Lower bus. perf. Higher bus. perf.
n	%	n	%
105	52.5	95	47.5
88	42.7	118	57.3
notes Overall percentage of correctly classified cases (54.9%).
table 7 Intercorrelations for dimensions of entrepreneurial orientation and measure
of business performance (N = 406)
Measure	Innovation Proactiveness Risk-taking Total eo
Business Performance	.32***	.33***	.26***	.37***
notes eo = Entrepreneurial orientation, ***p < .001.
Table 6 shows the classification analysis for the business performance;
54.9% of cases were classified correctly. The group with higher business
performance was more likely to be correctly classified (57.3% correct clas-
sifications) than the group with lower business performance (52.5% cor-
rect classifications).
entrepreneurial orientation and business
performance
A Pearson product-moment correlation coefficient was calculated to ex-
amine the relationship between entrepreneurial orientation and business
performance of smes in Taiwan. Table 7 shows the intercorrelations for
dimensions of entrepreneurial orientation and business performance.
Innovation was significantly positively correlated with business per-
formance (r = .32, p < .001). There was a medium, positive correlation
between innovation and the business performance. Proactiveness was
significantly positively correlated with business performance (r = .33, p
< .001). There was a medium, positive correlation between proactiveness
and business performance. Risk-taking was significantly positively cor-
related with business performance (r = .26, p < .001). There was a small,
positive correlation between risk-taking and business performance. Total
entrepreneurial orientation was significantly positively correlated with
business performance (r = .37, p < .001). There was a medium, positive
correlation between total entrepreneurial orientation and business per-
formance.
table 8 Regression analysis summary for scores on eo of smes in Taiwan predicting
business performance variable
Variable	B	SEB	ß
Eo-innovation	.14	.05	.18**
Eo-proactiveness	.17	.05	.20**
Eo-risk-taking	.06	.04	.08
notes R2 = .146, adjustedR2 = .14 (N = 404, p <.001); ***p < .001, **p < .01.
entrepreneurial orientation to business performance
A standard multiple regression was performed with business perfor-
mance as the dependent variable and scores on entrepreneurial orien-
tation as the independent variables. An analysis for evaluation of as-
sumptions was performed to reduce the number of outliers and improve
the normality, linearity, and homoscedasticity of residuals. With the use
of p < .001 criterion for Mahalanobis distance, two multivariate outliers
were found. After deleting these outliers, 404 cases without missing data
continued to be analyzed. A residual analysis was conducted to check as-
sumptions. To check the scatterplot of the standardized residuals and the
normal probability plot, assumptions about residuals were met.
Table 8 presents the correlations between the variables, unstandard-
ized regression coefficients (B), and standardized regression coefficients
(ß). This regression model was significantly different from zero, F (3,400)
= 22.81, p < .001.
For the two regression coefficients that differed significantly from zero,
95% confidence limits were calculated. The confidence limits for innova-
tion were .05 to .22; for proactiveness, they were .07 to .28. Two of the
independent variables contributed significantly to prediction of business
performance, innovation (sr2 = .02) and proactiveness (sr2 = .02). Al-
together, 14% (adjusted R2) of the variability in business performance
was predicted by entrepreneurial orientation scores on these three in-
dependent variables. Risk-taking was not a good predictor of business
performance.
leadership styles and entrepreneurial orientation
The relationship between the top-level managers' leadership styles and
the entrepreneurial orientation of smes in Taiwan was examined us-
ing Pearson product-moment correlation coefficient. Transformational
leadership was significantly positively correlated with innovation (r =
table 9 Intercorrelations for leadership styles and the eoq measures (N = 406)
Measure	TF	TA	PA
Eo-innovation	.22***	.13*	.03
Eo-proactiveness	.23***	.09	-.03
Eo-risk-taking	.14**	.05	.05
Total entrepreneurial	.24***	.11*	.02
orientation			
notes TF = transformational leadership, TA = transactional leadership, PA = passive-
avoidant leadership; * p < .05, ** p < .01, *** p < .001.
.22, p < .001), proactiveness (r = .23, p < .001), risk-taking (r = .14, p =
.004), and total entrepreneurial orientation (r = .24, p < .001). There was
a small, positive correlation between transformational leadership and all
dimensions of entrepreneurial orientation. Transactional leadership was
significantly positively correlated with innovation (r = .13, p = .01) and
total entrepreneurial orientation (r = .11, p = .029). There was a small,
positive correlation between transactional leadership and innovation or
total entrepreneurial orientation. There were no significant relationships
between passive-avoidant leadership and entrepreneurial orientation. As
shown in table 9, transformational leadership had a stronger relation-
ship on total entrepreneurial orientation than transactional leadership,
but with a small and positive correlation.
leadership styles to entrepreneurial orientation
A standard multiple regression was performed with the total entreprene-
urial orientation as measured by the eoq as the dependent variable and
scores on the mlq (transformational leadership, transactional leader-
ship, passive-avoidant leadership) as the independent variables. An anal-
ysis for evaluation of assumptions was performed to reduce the number
of outliers and improve the normality, linearity, and homoscedasticity of
residuals. With the use of p < .001 criterion for Mahalanobis distance,
no multivariate outliers were found. No cases had missing data and no
suppressor variables were found for the sample (N = 406). A residual
analysis was conducted to check assumptions. To check the scatterplot
of the standardized residuals and normal probability plot, assumptions
about residuals were met.
Table 10 indicates the correlations between the variables, unstandard-
ized regression coefficients (B), and standardized regression coefficients
(ß). This regression model was significantly different from zero, F (3,402)
table 10 Regression analysis summary for the top-level managers' leadership styles
predicting the total entrepreneurial orientation
Variable	B	SEB	ß
Transformational leader-	.62	.13	.30***
ship			
Transactional leadership	-. 18	.13	-.09
Passive-avoidant leadership	.07	.08	.05
notes R2 = .063, adjustedR2 = .056 (N = 406,p < .001). ***p < .001.
= 8.95 and p < .001. The regression coefficient of transformational lead-
ership was different from zero, and 95% confidence limits were .36 to .88.
Only transformational leadership contributed significantly to prediction
of the total entrepreneurial orientation, p < .001 and the semipartial cor-
relations sr2 = .05.
Altogether, 5.6% (adjusted R2) of the variability in the total en-
trepreneurial orientation was predicted by mlq scores on these three
independent variables. Transactional leadership and passive-avoidant
leadership were not good predictors of the total entrepreneurial ori-
entation. Hence, the transformational leadership of top-level managers
contributed the most to the total entrepreneurial orientation of smes.
leadership styles and entrepreneurial orientation to
business performance: discriminant function analysis
The researcher divided business performance into two categories by its
mean (M = 4.32): higher business performance and lower business per-
formance. A direct discriminant function analysis was performed using
three variables of the mlq and three variables of the eoq as predic-
tors of membership in the two groups. Predictors were transformational
leadership, transactional leadership, passive-avoidant leadership, inno-
vation, proactiveness, and risk-taking. The groups were higher business
performance and lower business performance. Two cases were identi-
fied as multivariate outliers with p < .001. After deleting these cases, 404
cases remained for analysis. An analysis for evaluation of assumptions
of linearity, normality, multi-collinearity, and homogeneity of variance-
covariance matrices did not violate multivariate analysis.
A discriminant function indicated a strong association between groups
and predictors, x2 (6) = 45.04 and p < .001. Table 11 presents the correla-
tion of predictor variables with discriminant functions, which suggested
that the good predictors for distinguishing between higher business per-
table 11 Correlation of predictor variables from mlq and eoq with discriminant
functions (function structure matrix) and standardized discriminant
function coefficients
Predictor variable	Correlation with	Standardized discriminant
	discriminant functions	function coefficients
	Function 1	Function 1
Transformational leadership	.54	.41
Transactional leadership	. 26	-.08
Passive-avoidant leadership	-.13	-.13
Innovation	.84	.55
Proactiveness	.76	.27
Risk-taking	•59	.20
table 12 Means and standard deviations of predictor variables from mlq and eoq		
as a function of business performance		
Predictor variable	Lower business perf.	Higher business perf.
	M SD	M SD
Transformational Leadership	2.92 0.52	3.01 0.49
Transactional Leadership	2.63 0.52	2.68 0.51
Passive-avoidant Leadership	1.40 0.63	1.37 0.62
Innovation	4.41 1.27	4.77 1.27
Proactiveness	4.26 1.15	4.56 1.17
Risk-taking	3.68 1.26	3-95 1-30
formance and lower business performance were transformational lead-
ership, innovation, proactiveness, and risk-taking.
Table 12 shows the innovation, F (1,402) = 33.68 and p < .001; the
proactiveness, F (1,402) = 27.40 and p < .001; and the risk-taking, F
(1,402) = 16.62 and p < .001. Innovation was the best predictor of group
membership (Wilks's a = .923). As shown in table 13, higher business
performance differs from lower business performance on four variables:
transformational leadership, F (1,402) = 13.86 and p < .001; innovation,
F (1,402) = 33.68 and p < .001; proactiveness, F (1,402) = 27.40 and p <
.001; and risk-taking, F (1,402) = 16.62 and p < .001. Innovation was the
best predictor of group membership (Wilks' a = .923).
Table 14 reports the classification analysis for business performance;
63.4% cases were classified correctly. The group with higher business per-
formance was more likely to be correctly classified (65.4% correct classifi-
The Relationships Among Leadership Styles... 271
table 13 Predictor variables from mlq and eo q in stepwise discriminant function
analysis
Predictor variable	Wilks' A	Equivalent F (1,402)
Transformational leadership	.967	13.86***
Transactional leadership	.992	3.21
Passive-avoidant leadership	.998	0.84
Innovation	.923	33.68***
Proactiveness	.936	27.40***
Risk-taking	.960	16.62***
notes ***p < .001.
table 14 Classification analysis for business performance
Actual group membership	Predicted group membership
Lower bus. perf. Higher bus. perf.
	N	n	%	n	%
Lower business performance	199	122	61.3	77	38.7
Higher business performance	205	71	34.6	134	65.4
notes Overall percentage of correctly classified cases (63.4%).
cations) than the group with lower business performance (61.3% correct
classifications).
Conclusion
In a comparison of three different leadership styles (N = 406), the mean
for transformational leadership (M = 3.00) is higher than the mean for
transactional leadership (M = 2.68) and for passive-avoidant leadership
(M = 1.37). The results are similar to the results of a study (N = 27,285)
by Avolio and Bass (2004) in which the mean for transformational lead-
ership (M = 2.85) was higher than the mean for transactional leadership
(M = 2.27) and for passive-avoidant leadership (M = .84). Furthermore,
a comparison of the three dimensions of entrepreneurial orientation (N
= 406) shows that the mean for innovation (M = 4.77) is higher than the
mean for proactiveness (M = 4.56) and for risk-taking (M = 3.95). Total
entrepreneurial orientation has a mean of 4.43 and a standard deviation
of 1.02. These results are similar to those of Covin and Slevin (1989); in
their results, the scale had a mean of 4.33 and a standard deviation of 1.23.
In another study, Covin et al. (2006) found that the scale had a mean of
4.05 and a standard deviation of 1.08.
The study confirmed the results of Covin and Slevin (1989) who found
that entrepreneurial orientation was positively related to performance.
Consistent with much of the literature reviewed (Smart and Conan 1994;
Wiklund and Shepherd 2005; Zahra and Covin 1995), the findings from
the current study suggest that high levels of total entrepreneurial orien-
tation may contribute positively to business performance. A compari-
son of the three dimensions of entrepreneurial orientation shows that
high levels of innovation and proactiveness may contribute positively to
business performance. The attributes that appear to contribute to high
business performance are innovation and proactiveness. Risk-taking is
not a significant contributor to predicting business performance, but it
is significantly positively correlated with business performance in this
study. Although risk-taking is considered an attribute of entrepreneur-
ship, successful entrepreneurs are not gamblers (Kuratko and Hodgetts
2001). Indeed, Drucker (1985) argued that successful entrepreneurs are
typically not risk takers. Entrepreneurs usually take carefully calculated
risks and avoid taking unnecessary ones (Begley and Boyd 1987). The re-
lationship between risk-taking and business performance is likely to be
affected by other influences such as environmental factors (Krauss et al.
2005; Kreiser et al. 2002).
Different leadership styles may affect performance. Transformational
leadership is significantly more correlated to the business performance
than transactional leadership and passive-avoidant leadership. Among
the three different leadership styles, transformational leadership is the
best predictor of the business performance. This study supports the po-
sition of Gardner and Stough (2002) that transformational leadership
is more effective than transactional leadership. Transformational lead-
ership is more strongly correlated than transactional leadership with
higher productivity and performance (Bass et al. 2003; Lowe et al. 1996).
Eggers and Leahy (1995) reported that both management and leader-
ship skills such as financial management, communication, motivation
of others, vision, and self-motivation play important roles in determin-
ing the growth rate of a small business. The results of this study con-
firmed these findings because key aspects of transformational leadership
include idealized influence, inspirational motivation, intellectual stimu-
lation, and individualized consideration (Avolio and Bass 2004). Finally,
transformational leadership with higher entrepreneurial orientation can
contribute to higher business performance. This study supports the idea
of entrepreneurial leadership, which is viewed to be more transforma-
tional than transactional in nature but with some fundamental differ-
ences (Thornberry 2006).
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How Internal and External Sources of Knowledge
Contribute to Firms' Innovation Performance
Anja Cotic Svetina
Igor Prodan
This paper investigates the extent to which different knowledge sources
contribute to firms' innovation performance. The empirical analysis
estimates the relationships in the structural model of the influence
of knowledge sources on innovative performance using data collected
through personal interviews at 303 firms. The results reveal that internal
sources have the most important influence on firms' innovative per-
formance and confirm that, in their innovation process, firms mostly
rely on knowledge developed through in-house r&d efforts, continu-
ous improvement, and internal education and training programs. The
data show that in-house learning is not sufficient for generating in-
novation and that firms need to supplement internal knowledge with
knowledge acquired outside the firm. They mainly need to secure links
with firms and institutions in the global environment if they want to
secure the inflow of new ideas and approaches that will eventually lead
to innovations.
Key Words: knowledge, innovation, structural equation modeling
jel Classification: 030, 031
Introduction
An interactive view of innovation has been developed within the frame-
work of a learning economy, in which innovation is seen as a techni-
cal and social process based on the complex interaction between firms
and their environment (Asheim and Isaksen 1997). Most authors agree
that the use of internal and external knowledge sources contributes pos-
itively to firms' innovation performance, but the relationship has been
empirically tested only to a limited extent (Capello 1999; Caloghirou,
Kastelli, and Tsakanikas 2004; Capello and Faggian 2005). This paper in-
vestigates the extent to which various knowledge sources contribute to
firms' innovation performance. More specifically, it identifies on the one
DrAnja Cotic Svetina is an Assistant at the Faculty of Economics,
University of Ljubljana, Slovenia.
Dr Igor Prodan is an Assistant Professor at the Faculty of Economics,
University of Ljubljana, Slovenia.
hand the level of importance of internal knowledge sources embodied
mainly in in-house r&d efforts. On the other hand, it looks at exter-
nal sources of knowledge and identifies how the use of local, national,
and international knowledge sources determines firms' innovation per-
formance. This paper extends the work of other scholars as to what are
the sources of innovation, by considering how knowledge sources at dif-
ferent spatial levels influence the innovation performance of firms. While
most authors analyzed the role of external knowledge sources in general
(Caloghirou, Kastelli, and Tsakanikas 2004; Willoughby and Galvin 2005;
Tsai and Wang 2007; Love and Mansury 2007), we divide them according
to the geographical proximity to the observed firm. As such, this is one
of the few empirical papers that assesses simultaneously to what extent
internal, local, national and international knowledge sources contribute
to firms' innovation.
The empirical analysis is based on a survey that was carried out in
seven European countries: the Czech Republic, Germany, Italy, Poland,
Romania, Slovenia, and the United Kingdom. The relationships in the
structural model of the influence of sources of knowledge on innovation
performance are estimated using data collected through personal inter-
views at 303 firms. The results reveal that internal sources have the most
important influence on firms' innovative performance and confirm that,
in their innovation process, firms mostly rely on knowledge developed
through in-house r&d efforts, continuous improvements and internal
education, and training programs. The data show that in-house learn-
ing alone is not sufficient for generating innovation and the firms need
to supplement internal knowledge with knowledge acquired outside the
firm. They mainly need to secure links with firms and institutions in the
extra-local environment in order to secure the inflow of new ideas and
approaches that will eventually lead to innovations.
The paper is structured in five sections. The next section presents
the theoretical framework on which the empirical analysis is based. The
main focus is on the literature describing the importance of internal and
external knowledge sources and how they contribute to firms' innova-
tive performance. Then four hypotheses are developed, which are later
empirically tested. The third section describes the methodology used, in-
cluding the sampling and data collection process, data analysis, and op-
erationalization and measure validation. The fourth section is dedicated
to presenting the empirical findings together with a graphic presenta-
tion of the structural model. The results are summarized and the main
findings discussed in the last section.
Acquiring Knowledge for Innovation:
Theory and Hypotheses Development
Until the 1980s, understanding of the innovation process was strongly
influenced by the linear model of innovation, which suggested that de-
velopment of innovations follows a straight research-to-market trajec-
tory. In this model a central role was given to r&d activity, and firms'
innovative performance was mainly seen as a consequence of r&d in-
vestment. This research-based and technocratic view of the innovation
process could not explain the success of several sme firms that had lim-
ited resources for in-house r&d but were able to base their competitive-
ness on constant innovation. This phenomenon of innovative smes has
become especially apparent in several sme clusters that have emerged
all over Europe and the rest of the world. Since then, several scholars
and practitioners have tried to reveal the dynamics behind small and
medium-sized firms' innovativeness. More than a decade ago it became
obvious that innovations rarely occur as creative acts of individual ge-
niuses, but more often as a result of interactive processes. Individuals
can not learn new things in a cognitive vacuum and learning always takes
place in relation to some kind of social context (Johnson 1992; Lundvall
1992). From the perspective of innovation, new knowledge is not only
developed in r&d departments but also in connection with ordinary
production activities of firms and other actors through the interactive
learning process (Eriksson 2005). Firms cooperate with their suppliers,
customers, knowledge institutions (universities, laboratories, etc.), and
even with their competitors when developing new products and services
or improving production processes. The interactive model of innovation
explains the process of innovation as a network of knowledge-flows both
within the organization, and in the relationship between the organiza-
tion and the environment (Santos 2000).
internal and external sources of knowledge
This section aims to show how complex the process of knowledge acqui-
sition is, and to present the idea that firms need to acquire new knowl-
edge from numerous internal and external sources in order to constantly
generate innovations and maintain their competitive edge.
According to the general trend towards more composite knowledge,
where new products and processes typically combine many technolo-
gies from several scientific disciplines, it is important to understand that
firms today can hardly learn and innovate in isolation (Pavitt 1998; John-
son, Loren, and Lundvall 2002). While in large firms information and
knowledge are still mainly transferred through functional interaction
among r&d, production, marketing, and organization departments and
functional teams (Capello 1999), small and medium-sized firms increas-
ingly need to rely on external knowledge sources. Accordingly, knowl-
edge sources can be firstly divided into internal and external sources,
whereas external sources can be further divided into local, national, and
international sources, depending on where the source of knowledge is
located (Belussi, McDonald, and Borras 2002). Internally, firms acquire
knowledge through in-house research and development activities and by
learning from continuous improvements in processes. Employee skills
represent another important source of new knowledge, and firms often
organize internal education and training programs in order to further
build and improve the internal knowledge base. If firms do not have ap-
propriate knowledge inside the firm, they can acquire it externally by
cooperating with customers and suppliers, as well as other firms, or by
forming partnerships with public, semi-public, and private institutions.
In terms of geographic location, these external actors can be located in
close geographic proximity (locally), somewhere in the country (nation-
ally), or elsewhere (internationally).
Among external sources of knowledge, inter-firm collaboration has
probably received the most widespread research attention. It is widely
recognized that the innovative process often involves interaction between
the manufacturer and users of products. Usually such interaction be-
tween producers and end users involves not only an exchange of tech-
nical knowledge but also important information about market require-
ments and trends. Another important source of knowledge comes from
the other side of the supply chain. Suppliers of equipment and mate-
rial (Geenhuizen 1997) can bring important insight into the organiza-
tion of production, logistics and other functions. But inter-firm coop-
eration extends far beyond the relationships that develop between sup-
ply chain partners. Studies of successful firms reveal that some sort of
collaborative arrangements develop between business partners as well as
between competitors. For example, a study of the Cambridge region re-
vealed that 76% of firms possess close links with other firms (Keeble et al.
1998). When analyzing the nature of inter-firm cooperation they identi-
fied everything from joint ventures, subcontracting, and research collab-
orations to the sharing of equipment and information about customers.
Accordingly, we perceive both vertical as well as horizontal inter-firm re-
lationships as sources of important external sources of knowledge and
interactive learning (Camagni 1993; Yeung 2005; Steiner and Hartmann
2006).
Knowledge exchange not only appears between firms but can often be
found between firms and institutions. Universities, research institutes,
science parks, incubators, and other knowledge institutions are actively
involved in a set of relationships occurring in the business environment
(Gunasekara 2006) and are particularly seen as lead players in the in-
novative activity of firms providing scientific research inputs for inno-
vating firms (Keeble and Wilkinson 2000). According to Gambarotto
and Solari (2004), in addition to channeling information and knowl-
edge, support organizations can also help translate academic codified
knowledge into practical and accessible know-how. In line with the mod-
ern understanding of innovation, the research process is oriented toward
problem-solving and as such requires two-way research interaction be-
tween knowledge organizations and industry actors combined with sev-
eral other institutions.
Inter-firm collaboration, as well as partnerships with institutions,
were long believed to be mainly limited to the local level and were studied
within the context of clusters. However, with globalization and advances
in information and communication technology, the geographic scope of
this interaction is widening and often spreads across national borders.
If firms want to succeed in the innovation race, they need to have ac-
cess to the most advanced technical and organizational knowledge in
their fields, which means they have to search for appropriate knowledge
with no regard to its location. The use of geographically close sources
has several benefits that stem from constant face-to-face interactions,
knowledge spillovers, and the transmission of tacit knowledge (Cam-
agni 1991; Keeble 2000; Capello and Faggian 2005). However, this does
not imply that the mere use of local knowledge sources is sufficient in
terms of knowledge creation and innovation. Research shows that lim-
iting knowledge acquisition to the local level can lead to a lock-in effect
(Grabher 1993; Keeble and Wilkinson 1999; 2000). In order to maintain
a constant inflow of new knowledge, firms need to nurture links inside,
as well as outside, the cluster.
toward the research hypotheses
The following paragraphs present the main theoretical arguments for the
role of knowledge sources in firms' innovation performance, and develop
four hypotheses.
In-house r&d efforts have been consistently proven to contribute to
firms' innovative potential. A systematic review of studies investigating
the use of knowledge in smes has shown that internal managerial and
entrepreneurial teams, as well as other employees, play a crucial role in
knowledge creation and, consequently, innovation (Thorpe et al. 2005).
Additionally, a firm's in-house expertise for r&d has a considerable pos-
itive effect on the absorptive capacity of firms. Cohen and Levinthal
(1990) define absorptive capacity as 'the ability of a firm to recognize
the value of new, external information, assimilate it, and apply it to com-
mercial ends.' This means that continuous improvements in its internal
knowledge base are also important for increasing a firm's capability to
assimilate and transform external knowledge and information into new
products, services, and processes. As Lundvall and Nielsen (1999) argue,
a strong internal knowledge base is the key to successful innovation. In
line with their arguments, we posit that the greater the use of internal
knowledge sources, the more innovations the firm will be able to create,
as well as exploit knowledge from external sources and transform it into
innovation. Therefore, we have formulated the following hypothesis:
H1 The extent of usage of internal knowledge positively influences firms'
innovative performance.
What collective and interactive learning literature argues in general
is that a firm's learning capacity does not depend solely on individual
skills and the organization of the firm (internal to the firm), but it is also
context dependent on the institutional set-up of its business environ-
ment (Lorenzen 1998; Tomassini and Sarcina 2005). In recent decades
companies have been facing an increase in uncertainty and risk (Geen-
huizen 1997). Firms in many industries are facing a turbulent environ-
ment with changes taking place in market, technology, and industrial
organization. Responding to various uncertainties, companies have in-
creasingly externalized their sources of knowledge. In order to increase
or deploy their own knowledge effectively, firms often need to supple-
ment their knowledge with that of other firms and organizations, which
often happens in some form of collaborative arrangements. The growing
importance of inter-organizational collaboration can be explained by the
nature of contemporary knowledge (Dunning 2000): the development
of new knowledge can be highly expensive; the outcome of much in-
vestment in augmenting knowledge (by r&d) is highly uncertain; many
kinds of knowledge become obsolete quite quickly; and complex prob-
lems require multi-disciplinary team solutions. In addition, competitive
pressures are forcing firms to introduce new products and services to
the market at an increasing pace, and for many (especially small and
medium-sized) firms it is impossible to rely only on internal resources
for necessary knowledge production. Consequently, firms and other or-
ganizations are increasingly engaging in inter-organizational coopera-
tion projects (Eriksson 2005).
There are several institutional environments in which firms acquire
knowledge and learn. Lundvall (1992) emphasizes the national level as
an institutional framework for learning and innovation, because of its
homogeneity with respect to culture, technical and educational institu-
tions, and historically-built relations between actors and firms. Other
researchers focus on the regional and local levels as the most impor-
tant environments for knowledge acquisition and innovation. Recently
an increasing number of scholars have proven that firms often search for
knowledge internationally (Malmberg and Power 2005). What these the-
ories have in common is the fact that external knowledge sources provide
an important complement to in-house learning and innovation efforts,
and thus contribute to improved innovative performance (Caloghirou,
Kastelli, and Tsakanikas 2004).
The importance of inter-organizational relationships has been mainly
developed and studied in the context of localized clusters, where a num-
ber of firms, knowledge and research institutions, and other actors are
located in close geographic proximity. The literature on localized and
collective learning argues that the local level is the most appropriate en-
vironment for knowledge exchange and interactive learning due to cul-
tural, social, and organizational proximity (Lundvall 1992; Belussi and
Pilotti 2000; Steiner 2006), which has led to formulation of the following
hypothesis:
h2 The more a firm uses local sources of knowledge, the more it develops
knowledge sharing that positively contributes to innovative perfor-
mance.
The main problem of the localized learning literature is that it has
sometimes been read in a way that places local knowledge acquisition as
a superior form that might sometimes take the place of knowledge acqui-
sition and learning at the national and international levels; some authors
even believe that it can replace internal r & d efforts (Capello and Faggian
2005). This stream of literature describes clusters and other local net-
works as being somehow self-sufficient in knowledge terms. This has led
to a rather heated debate in recent years (Malmberg and Maskell 2006),
proving that interactions with distant partners are at least as important
as those with local actors. Several authors have empirically proven that
learning might be best understood as a combination of close and distant
interactions (Malmberg and Maskell, 2006; Wolfe and Gertler 2006; Brit-
ton 2003; Cumbers, MacKinnon, and Chapman 2003; Henry and Pinch
2001; Tödling and Kaufmann 1999).
Recently many authors have stressed the importance of linkages with
external firms, institutions, or even networks, which provide access to
external knowledge and technology, and prevent the lock-in effect. The
most recent contribution to this discussion comes from Malmberg and
Maskell (2006), who submit that neither the argument for localized in-
teractive learning nor the existence of localized capabilities in any way
presupposes that most knowledge exchange and learning interaction
should be local. They believe that extra-local knowledge flows can be
expected to connect to the local knowledge flows so that the two become
mutually reinforcing. This happens when the external sources 'pump'
information and news about markets and technologies into the local en-
vironment and consequently intensify the local interaction and benefit
the local actors.
The main idea of this literature is that intense localization within a cer-
tain local environment does not mean isolation from the extra-local en-
vironment. Firms form networks and partnerships with firms and other
organizations at the local, national and international levels in order to
enhance their knowledge base and innovation potential. Business and
social contacts can be more frequent, intensive, and easier to maintain
if they are facilitated by proximity (all types); however, firms must nur-
ture their relationships with firms and organizations outside the local
area and even try to engage in global networks (Malmberg and Maskell
2006; Bathelt, Malmberg, and Maskell 2002). This will provide them with
access to information on rapidly changing technologies and market op-
portunities and provide a constant influx of new knowledge needed in
the innovation process. The role of the national knowledge sources was
extensively discussed in the literature dealing with national systems of in-
novation (Lundvall, 1988; 1992; Lundvall et al. 2002), while the role of in-
ternational sources has mainly been studied in the context of r&d part-
nerships (Knudsen 2006). Based on this literature, both national and in-
ternational sources of knowledge are expected to positively contribute to
firm's innovation performance. As Simmie (2006) suggests, innovation
must be understood in terms of trading nodes in an international system
that encompasses local, national and international knowledge spillovers
and multilayered economic linkages that extend over several different
spatial scales.
The above discussion underpins the last two hypotheses:
H3 The more a firm uses national sources of knowledge, the more it de-
velops knowledge sharing that positively contributes to innovative
performance.
H4 The more a firm uses international sources of knowledge, the more it
develops knowledge sharing that positively contributes to innovative
performance.
Methodology
The methodology is discussed in terms of the sampling and data collec-
tion process, data analyses, operationalization, and measure validation.
sampling and data collection process
Data for testing the structural equation model for explaining the in-
fluence of knowledge sources on innovation activity were collected
within the research project weid (West-East id: Industrial Districts' Re-
Location Processes; Identifying Policies in the Perspective of the Euro-
pean Union Enlargement) conducted under the 5th eu Framework Pro-
gram. Eleven European research partners were included in the project:
Fondazione Istituto Guglielmo Tagliacarne (Italy), Eurochambres Aisbl
(Belgium), Istituto per lo Sviluppo della Formazione dei Lavoratori
(Italy), Libera Universita Internazionale degli Studi Sociali 'Guido Carli'
(Italy), Manchester Metropolitan University (United Kingdom), Om-
nimotio s. r. o. (Czech Republic), Landesinstitut Sozialforschungsstelle
Dortmund (Germany), University of Ljubljana (Slovenia), University
of Reading (United Kingdom), University of Roskilde (Denmark), and
University of Aurel Vlaicu in Arad (Romania). Based on the literature
review, interviews with managers, and work with focus groups within
the weid research group, a questionnaire for in-depth interviews was
developed. The questionnaire was initially prepared in English and then
first translated into the local languages (Czech, German, Italian, Polish,
Romanian, and Slovenian), and after that back-translated into English
(Brislin 1970; Brislin 1980; Hambleton 1993). The translation followed
the 'etic approach' - an approach where there is little or no attempt to
decenter or adapt the measure to another cultural context (Craig and
Douglas 2005). In-depth interviews with top executives from manufac-
turing firms from the Czech Republic, Germany, Italy, Poland, Roma-
nia, Slovenia, and the United Kingdom were conducted on the basis of
the structured questionnaire developed. For the analyses, 303 usable re-
sponses were obtained. The composition of the sample was comparable
to the population.
data analyses
Reliability was assessed using Cronbach's (1951) alpha. Construct and
discriminant validity, as well as convergent validity, were assessed us-
ing exploratory and confirmatory factor analysis (Floyd and Widaman
1995). Exploratory factor analysis and reliability analysis was conducted
in spps. The eqs Multivariate Software version 6.1 (Bentler and Wu
2006) was utilized for confirmatory factor analysis and testing of the
proposed structural model. Since a small amount of non-normality was
found in the data, the Elliptical Reweighted Least Square (erls) estima-
tion method was used (Sharma, Durvasula, and Dillon 1989). As recom-
mended by Shook, et al. (2004), the fit of the model was assessed with
multiple indices: nfi (the normed-fit-index), nnfi (the non-normed-fit
index), cfi (the comparative fit index), gfi (the goodness-of-fit index),
srmr (the standardized root mean square residual), and rmsea (the
root mean square error of approximation). Values of nfi, nnfi, cfi, and
gfi greater than 0.90 indicate a good model fit (Hair et al. 1998; Byrne
2006). Hu and Bentler (1999) suggest that values of srmr smaller than
0.08 indicate an acceptable fit. Values of rmsea less than 0.05 indicate
good fit, and values as high as 0.08 represent reasonable errors of approx-
imation in the population (Browne and Cudeck 1992). The chi-square is
reported, but is not given major consideration because it is highly sen-
sitive to sample size and the number of items in the model (Bentler and
Bonett 1980).
operationalization and measure validation
In this study, independent and dependent variables were measured
through scales previously tested and developed by the weid research
group.
Internal Sources of Knowledge
Internal sources of knowledge were measured with six items. Respon-
dents were asked to indicate (on a 5-point Likert-type scale ranging from
'not important at all' to 'very important') how important the following
internal sources of knowledge are for their company: knowledge gained
through in-house research and development (1NT01), knowledge gained
from continuous improvement of production processes (int02), knowl-
edge developed through their company's internal education and train-
ing programs (1NT03), organizational skills learned from continuous im-
provement of their production processes (1NT04), organizational skills of
the professional managers within their local company (1NT05), and or-
ganizational skills gained from their company's internal education and
training programs. The factor analysis indicated that all factor loadings
were above 0.4 and significant. Cronbach's alpha of 0.80 indicates strong
internal consistency of six items operationalized to measure this con-
struct.
Local, National, and International Sources of Knowledge
Local, national, and international sources of knowledge were each mea-
sured with 10 items. Respondents were asked to indicate (on a 5-point
Likert-type scale ranging from 'not important at all' to 'very impor-
tant') how important the following local, national, and international
sources of knowledge are for their company: knowledge derived from
interactions with clients and/or suppliers (local clients and/or customers
- loc01; national clients and/or customers - NAT01; and international
clients and/or customers - 1NAT01), knowledge derived from cooper-
ation with other companies (loc02, NAT02, and INAT02), knowledge
gained from interactions with public institutions such as universities,
public research centers, local government, and so on (loc03, NAT03,
and INAT03), knowledge gained from interactions with semi-public in-
stitutions such as chambers of commerce, industry associations, trade
unions, and so on (loc04, NAT04, and INAT04), knowledge provided by
consultants and private research centers (loc05, NAT05, and INAT05),
organizational skills gained from interactions with clients and/or sup-
pliers (loc06, nat06, and inat06), organizational skills gained from
cooperation with other companies (loc07, NAT07, and INAT07), organi-
zational skills learned from interactions with public institutions such
as universities, public research centers, local government, and so on
(loc08, NAT08, and inat08), organizational skills learned from inter-
actions with semi-public institutions such as chambers of commerce,
industry associations, trade unions, and so on (loc09, NAT09, and
INAT09), and organizational skills learned from consultants and pri-
vate research centers (loc10, nat10, and inat10). The factor analysis
indicated that all factor loadings were above 0.4 and significant for all
three constructs. To test for convergent validity of the constructs and to
compare the one-factor structure with the three-factor structure (where
factors are correlated), the confirmatory factor analysis was conducted.
The results showed that one-factor structure is not appropriate because
of the overall poor model fit (chi-square = 1420.029, 368 df, probability
0.000; nfi = 0.80; nnfi = 0.81; cfi = 0.84; gfi = 0.69; srmr = 0.12;
and rmsea = 0.10). The confirmatory factor analysis showed that the
three-factor structure fits the data reasonably well, with the following
fit indices: chi-square = 681.457, 365 df, probability 0.000; nfi = 0.90;
nnfi = 0.94; cfi = 0.95; gfi = 0.82; srmr = 0.08; and rmsea = 0.05.
Cronbach's alphas of 0.85 (local sources of knowledge), of 0.86 (national
sources of knowledge), and of 0.86 (international sources of knowledge)
indicate strong internal consistency of items operationalized to measure
these constructs.
Innovation Performance
Innovation performance was measured with five items. Respondents
were asked to indicate whether their company had registered patents
abroad in the last three years (ip01), and to indicate whether their com-
pany had introduced or adopted any major changes to their products
(ip02), processes (ip03), organization of production (ip04), and organi-
zation of sales and distribution (ip05). The factor analysis indicated that
all factor loadings were above 0.4 and significant. Cronbach's alpha of
0.75 indicates strong internal consistency of five items operationalized to
measure this construct.
Control Variables
Control variables were also included and operationalized as follows: (1)
firm's size was operationalized as the number of employees, and (2) the
region was operationalized as a dichotomous variable, where '0' repre-
sented western European countries (Italy, Germany, and United King-
dom) and '1' represented eastern European countries (Czech Republic,
Poland, Romania, and Slovenia).
Findings
The structural relationships in the model of the influence of sources of
knowledge on the innovation performance were estimated using the El-
liptical reweighted least square (erls) method in eqs 6.1 (Bentler and
Wu 2006). eqs reported that parameter estimates appeared in order,
and that no special problems were encountered during the optimization.
The resulting model goodness-of-fit indices indicated a moderately good
model fit (chi-square = 1600.305, 812 df, probability 0.000; nfi = 0.86;
nnfi = 0.92; cfi = 0.93; gfi = 0.76; srmr = 0.08; and rmsea = 0.06).
The variance explained for the innovation performance was 20%.
The model, which includes hypothesized relationships and results of
the model test, is depicted in figure 1. An examination of our hypotheses
is presented in the following section.
hypotheses testing
Hypothesis H1 proposed that the extent of the usage of internal sources
of knowledge is positively related to the innovation performance. The
results presented in figure 1 show that the internal sources of knowledge
have a significant, positive, and high path coefficient of 0.31. The result
thus provides strong support for hypothesis H1.
Hypothesis H2 proposed a positive relationship between local sources
of knowledge and firms' innovation performance. Hypothesis H2 was
not supported by the findings (significant standardized path coefficient
of -0.26), because the result was the opposite of what was predicted, in-
dicating that local sources of knowledge are negatively related to innova-
tion performance.
Hypothesis H3 assessed the relationship between national sources of
knowledge and firms' innovation performance. Hypothesis H3 was not
supported by the findings (non-significant standardized coefficient of -
0.01).
Hypothesis H4 predicted that the extent of international sources
of knowledge would be positively related to firms' innovation perfor-
mance. The results indicate a significant relationship between interna-
tional sources of knowledge and firms' innovation performance (positive
significant standardized coefficient of 0.25). The results thus support hy-
pothesis H4.
other findings
Other findings will be discussed in terms of the impact of control vari-
ables and the relationships between variables.
The impact of firm size and region as a dichotomous control vari-
able was assessed (western European countries versus eastern European

CJNATOT}.	i 1 1
<jNAroT>	
	«-0.47».
	«-0.67«'
(j^ATOT^)'	
CJNATOT>-	-16,* -
CJnatiO")-	
FIGURE 1
The model of the influence of sources of knowledge on the innovation
performance (bolded parameters are fixed; * sig. < 0.05)
countries). Although the model fit indices and the structural coefficients
of the relationship between independent variables and innovation per-
formance did not reveal substantial variations with the introduction
of control variables, both control variables were found significantly re-
lated to the innovation performance. The results indicate that firms from
eastern European countries are significantly less innovative than firms
from western European countries (negative significant standardized co-
efficient of -0.19), and that larger firms are significantly more inno-
vative than smaller firms (positive significant standardized coefficient
of 0.20).
The results also show that internal, local, national, and international
sources of knowledge are significantly correlated among each other.
While the correlations between internal and international, local and in-
ternational, and national and international sources of knowledge were
moderate (correlation coefficient of 0.25, 0.35, and 0.44 respectively),
the correlations among internal, local, and national were somewhat
higher (correlation coefficient between 0.54 and 0.57). Nevertheless,
multi-collinearity was not detected among any of the variables in the
multivariate model.
Discussion and Conclusion
The results presented in the previous section reveal that internal knowl-
edge sources are only some of the sources of innovation. Our research
confirmed that in-house learning is crucial for firms' innovation perfor-
mance; however, interactive learning outside the firm also significantly
contributes to innovativeness. According to these results, it is mainly co-
operation with international business partners that contributes to inno-
vation.
The significant, positive, and high path coefficient confirms the im-
portance of in-house r&d activities, continuous process improvements,
and internal education programs, which together boost firms' innova-
tiveness. This means that innovation performance to a great extent de-
pends on a firm's own efforts. This is not surprising, given the fact
that innovations strongly influence a firm's competitive position in the
market. Consequently, firms try to keep the innovation inside the firm,
mainly relying on internal knowledge sources. Know-how historically
was - and in large measure remains - a kind of knowledge developed
within the confines of a firm (already discussed in Hudson 1999), and
our results have proven that the boundaries of the firm are still signif-
icant for knowledge related to innovations that are central to the core
competencies and strategic goals of the company.
Nevertheless, the increasing complexity of the knowledge base upon
which the production process depends is increasing the social division of
labor in knowledge production, yet is also resulting in growing long-
term cooperation between firms (Hudson 1999). According to local-
ized learning literature, we expected local knowledge sources to posi-
tively contribute to firms' innovation performance; however, the results
proved the opposite. The findings might at first seem surprising because
they indicate that the use of local knowledge sources impedes innova-
tion. However, much of the literature warns that sole dependence on
local knowledge sources can lead to the lock-in effect, whereby firms
are 'locked' into the existing technological trajectory of the local envi-
ronment and are unable to continuously develop new products and ser-
vices and implement innovations in processes and organization (Visser
and Boschma 2004; Malmberg and Maskell 2006). Camagni (1991) has
already stressed that firms need linkages with the external business en-
vironment. Especially in times of rapid technological change, external
(non-local) links might provide local firms with the complementary
assets that are needed to adapt to the changing economic and techno-
logical environment. In areas of production characterized by fast in-
novation and technological change, 'local firm involvement in wider
national and global networks is absolutely essential for long-term re-
gional growth,' and 'the milieu has to open up to external energy in
order to avoid 'entropic death' and a decline in its own innovative ca-
pacity' (Camagni 1991, 139). Our results are not in line with the older
literature on localized learning (Capello 1999), which often positioned
learning at the local level as somehow superior to that at other spa-
tial levels. Nevertheless, our study confirms what most of the recent
literature is arguing by saying that innovation performance is a result
of combining several internal as well as external knowledge sources,
the latter coming from different geographical levels. Local knowledge
sources are important for firms to a certain extent, as geographic proxim-
ity and concentration of firms can provide enormous opportunities for
the transmission of sticky, non-articulated forms of knowledge between
firms (Tödling, Lehner, and Trippl 2004). However, localized learning
does not necessarily lead to innovation. Our results indicate that ac-
cess to codified external knowledge should be secured through inter-
action with firms and institutions outside the local environment, and
we show that new value is created by combining these various types of
knowledge.
In today's globalized economy, where supply chains are distributed all
around the globe and specialized knowledge and research institutions
are scattered in numerous locations, there is no reason to believe that
a firm will find the precise knowledge needed in its innovation process
within the local environment. Accordingly, firms search for the necessary
knowledge elsewhere and often look for appropriate innovation partners
irrespective of the geographic space. While our research did not reveal a
significant influence of national knowledge sources, it has proven that
international sources have a strong, positive, and significant influence
on firms' innovation performance. Keeble and Wilkinson (2000) have
already supported these ideas with the empirical findings of the Euro-
pean network. Numerous firms possess close functional links with firms
and knowledge centers in their countries and abroad, and view such
wider networks as very important for successful research and innova-
tion. Extra-local networking appears to be an important process whereby
high-tech firms sustain their innovative activity and competitive advan-
tage.
Our research confirms that firms need to incorporate the internal
learning process with knowledge acquired outside the firm. They need
to secure extra-local links in order to secure the inflow of new knowl-
edge needed in the innovation process and prevent the lock-in effect.
As Oinas and Malecki (2002) suggest, the innovation system can be un-
derstood as being internationally distributed and not only as an activity
primarily confined within a given local environment. In line with their
approach, Simmie (2006,133) suggests that 'innovation must be under-
stood in terms of trading nodes in an international system that encom-
passes both local and international knowledge spillovers and multilay-
ered economic linkages extending over several different spatial scales.' To
sum up, one can conclude that internal learning and interactive learn-
ing with firms and institutions in a wider business environment mutu-
ally reinforce each other and bring optimal results in terms of innova-
tion performance. In this respect, our results are in line with existing
studies (Caloghirou, Kastelli, and Tsakanikas 2004; Love and Mansury,
2007) that verify the importance of external sources and imply that in-
novations come from a number of sources and develop in a number of
ways (Willoughby and Galvin 2005). However, those studies mainly fo-
cus on the type of sources (for example suppliers and customers, scien-
tific system, public institutions, etc.) but do little to explain how location
of those knowledge sources influences the innovation performance of
firms. In this respect our study brings additional insight into the com-
plex process of innovation and proves that not all external knowledge
sources are equally important for innovation. According to our results,
firms need to establish and nurture collaboration with different partners
in the wider international environment in order to boost their innova-
tiveness.
Although this study has many strengths, it also has some limitations
that need to be acknowledged. Firstly, with regard to local knowledge
sources, the problem of knowledge internalization deserves mention;
that is, when firms overestimate the role of in-house activities and down-
grade the role of the local environment in which they operate. The
knowledge exchange between local firms and institutions mainly hap-
pens in a socialized way in the form of knowledge spillovers. As soon as
a firm acquires this local knowledge, it incorporates it into the existing
knowledge base, making it internal to the firm (Henry and Pinch 2000;
Cole 2007). Accordingly, firms might underestimate the importance of
being located in the local environment, because they take for granted
the benefits of the specialized local labor market, the proximity of simi-
lar firms, and close linkages with local universities and other knowledge
organizations. Secondly, the model of the influence of sources of knowl-
edge on innovation performance is not comprehensive (it includes a
limited number of elements in order to make the empirical examination
feasible) because it ignores some other factors that influence innova-
tion performance. Thirdly, although the causal directions hypothesized
in the model were suggested by the theory, the cross-sectional nature
of this study cannot prove the causation but can only support a set of
hypothesized paths (Kline 2005). Therefore, the possibility of reverse
causality cannot be eliminated.
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Ensuring Professionalism ofthe External
Evaluation Commission: The Slovenian Case Study
Karmen Rodman
Nada Trunk Širca
In 2006-2007, the Slovenian higher education (he) system took the first
steps toward building a national model of institutional external evalu-
ation (iee), which would be comparable with other European mod-
els. In the first part of the article, the authors discuss the main ten-
dencies within the European he area. This is followed by an outline
of the developments in the field of quality assurance within Slovenian
he, stressing the years 2006 and 2007. The scientific contribution of the
article lies in the evaluation outcomes of the national pilot iees, with
focus on the professional competences of the External evaluation com-
mission (eec) members. Observation results stress the importance of
the proper training of eec members. The authors propose that a sys-
tematic follow-up on the eec work needs to be established and a code
of ethics drawn up, highlighting the preferred values and principles of
eec members.
Key Words: quality assurance, higher education, external evaluation,
institutional evaluation, external evaluation commission
jel Classification: M42,128
Introduction
The present article is the result of the research, implemented during the
national pilot iees in the period 2006-2007. The Slovenian developing
he quality assurance system uses as its reference points the guidelines
provided by various European institutions for quality assurance in he.
But in these documents little attention is paid to the eec members train-
ing. It is indispensable to underline that the professional competences of
the eec members' has a direct impact on the hei representatives' per-
ception of iee. The role of the eec in iee, especially during a visit to the
hei, is the key to successful understanding, experiencing and acceptance
Karmen Rodman is a Lecturer at the Faculty of Management Koper,
University of Primorska, Slovenia.
Dr Nada Trunk Širca is Director ofthe Euro-Mediterranean
University, Slovenia.
of the iee process and its outputs, provided by the representative of the
evaluated heis.
This was one of the reasons why the evaluation case study of pilot na-
tional iees in four evaluated heis was carried out. Using observation as
their research method of choice, the research team sought the answers
to the question: What were the fields, stages and tools that should have
been improved in pilot iees? Before the observation began, an observa-
tion checklist had been drafted to ensure a coordinated approach of all
observers involved. By the triangulation of three methods (observation,
questionnaire survey and experience presentations) we tried to ensure a
correct understanding of the conclusions made by observers and sustain
the findings.
The authors have come to the conclusion that, since the main inter-
est of pilot iees lied in the assessment of national iee tools, the pro-
fessional competences of the eec members had been partly neglected.
Observation results thus stress the importance of the proper training of
eec members in research methodology, he activities and the related na-
tional legislation, besides the iee procedures. The authors propose that
a systematic follow-up on the eec work needs to be established and a
code of ethics drawn up, highlighting the preferred values and principles
of eec members. In spite of all that, the development, improvement and
comparability of the iees and the eec members are threatened as long as
the national evaluation body is not affiliated in the European Association
for Quality Assurance in Higher Education (enqa) and is not present in
the European Quality Assurance Register (eqar).
The potential future researches are presented in the last section.
Theoretical Framework
quality assurance in european higher education
European he systems are facing many challenges, arising from their na-
tional or international environments (Faganel, Trunk and Dolinšek 2005,
317; Srikanthan and Dalrymple 2003,126): liberalisation and the lifting of
boundaries in the labour market, employment and education, market-
isation (Logaj and Trnavcevic 2006, 79-80) and the fading of the divide
separating the public sector from the private, the transition of heis from
their status as elite institutions to mass institutions, and the adoption of
life-long learning. The emergence of new educational programmes and
heis is creating confusion and uncertainty for different he stakehold-
ers. Simultaneously, the governments provide fewer funds (per capita)
to meet those challenges.
The increasingly competitive world requires development of knowl-
edge society, and one of the Bologna process objectives is to provide the
guidelines for quality assurance to the national quality assurance systems
and likewise individual institutions for quality assurance. In addition
to the Bologna declaration (1999), a more significant quality assurance
orientation in the main European documents can be noted. Following
the Rectors' Meeting in Salamanca, Prague Communique, Berlin Com-
munique, Bergen Communique and London Communique (see http://
www.ond.vlaanderen.be/hogeronderwijs/bologna/documents/), quality
in he becomes the core notion in establishing a coordinated European
he area.
West Europe is trying to upgrade (self-)evaluations with institutional
and programme accreditation. In the mirror are more managerial and
leadership approaches to run the heis. In East (transition) Europe the
institutional and programme accreditation guarantees for the quality, al-
though the heis and the environment are not satisfied enough. The heis
need the support in undertaking the challenges. They do not demand
judgments on teaching and learning quality, but they ask for help to de-
velop and improve the heis' strategic and quality management. Support
of institutional and programme development is the main reason for im-
plementation of (external) evaluations in these countries. In 'Bologna'
countries the target is more the establishment of the European quality
network, as enqa and Agencies' network, than the improvement of the
existing national systems of accreditation and evaluation. In some Euro-
pean countries quality assurance is an internal responsibility of each hei
and is based on an internal evaluation of the institution's programmes. In
other countries quality assurance system incorporates an external eval-
uation or accreditation. In the first case, external peers evaluate pro-
grammes and institutions, while, in the second case, an external inde-
pendent agency grants a specific 'quality label' to programmes and in-
stitutions which have met a set of pre-defined requirements (Orsingher
2006,1).
Bearing in mind the autonomy of heis and considering the demand
of the environment for transparency and accountability, the education
sector has recognized a need for developing shared criteria and a com-
mon methodology in quality assurance. At the same time it is important
to leave enough room for innovation and diversity, because of the na-
tional policy differences. Consequently, the Standards and guidelines for
quality assurance in the European higher education area emerged, for-
mulated by the enqa in cooperation with the European university asso-
ciation (eua), European association of institutions in higher education
(eurashe) and the European student information bureau (esib, today
European students' union). The standards were adopted in the Berlin
Communique (2003).
'There is no uniform model of he quality assessment in the eu [Eu-
ropean Union], which would be agreed-upon, and it cannot be expected
either - because this is the domain of national affairs, even though they
do maintain a degree of international comparability' (Dolinšek, Trunk
Širca, and Faganel 2005, 20). While many differences in implementation
practices remain, various movements for the development of national
and international quality assurance systems can be noticed, showing
agreement on some common principles. These take the form of guide-
lines, drafted by European institutions for quality assurance. The doc-
uments are discussed in this article, following two assumptions. First,
these guidelines are an optimal product of the experiences, concerning
good practice. Second, these institutions and national agencies act as
independent bodies, so their work is primarily targeted at creating iee
strategies for achieving a maximum of positive effects in each hei as well
as its wider environment.
external evaluation commission
in european higher education
Seen as a tool for quality assurance, iee has many purposes. First, the
iee shows accountability towards various stakeholders. Accountability is
interpreted not just towards the funding authorities, looking for a good
return on investments - as value for money, but as understanding quality
as transformation of the participants that are potentially capable of con-
sidering the concerns of all stakeholders' groups (Srikanthan and Dal-
rymple 2003, 128). Unfortunately, this interpretation has been missing
from all approaches to quality in he so far (Harvey 1998; Srikanthan and
Dalrymple 2003,128).
The very important purpose of iee is to improve the capability of sin-
gle heis to define their target and choose the most suitable strategies, but
having regard to the stakeholders' opinion and requirements. iee facili-
tates the improvement cycle of single heis, because it serves as a diagnos-
tic instrument and means of producing guidelines or planning inputs.
This can in turn become a strategic tool of hei operation. As many au-
thors suppose, the introduction of iee process and methodology enables
transparency within European higher education and improves interna-
tional credibility (Dolinšek, Trunk Širca, and Faganel 2005, 20), whilst it
also provides the basis for funds allocation (Cuš 2006). The purpose is
also to reinforce institutional development by disseminating examples of
good practice in the areas of internal quality management and strategic
change (eua 2005,4).
The enqa's standards and guidelines see the use of the site-visits as
one of the widely-used elements of external review processes (2007, 20-
21). The role of the eec in the iee, especially during a visit to the hei,
is the key to a successful understanding, experiencing and acceptance of
the iee process and its outputs from the hei representatives. The pro-
fessional competences of the eec members have a direct impact on the
hei representatives' perception of iee.
The eua is trying to ensure the quality of its eec members by intro-
ducing a requirement that each member be previously appointed rector
or prorector and have successfully passed the examination (Kralj 2006,
4). Hereby the eua model disregards the invaluable insight of other
stakeholders, which can significantly contribute to a comprehensive view
of the hei quality.
In addition to general criteria for eec members' selection (finheec
2006, 19; enqa 2007, 20), many institutions for quality assurance in
higher education invest into training of the eec members for their tasks,
considering the differences in their backgrounds, which range from the
corporate sector to the non-profit sector. In addition, the majority of eec
members are students with limited experiences and stakeholders with
varying degrees of knowledge in national he legislation. Rossi's claim
(2004, 27) that 'ideally, every evaluator should be familiar with the full
repertoire of social research methods' poses a vague outline of the qual-
ifications of eec members. finheec (2006, 20) arrange a special train-
ing for the eec members, with the focus on the objectives and different
phases of the iee process, the responsibilities of the eec and iee meth-
ods. At the same time, the eec members become acquainted with the
situation in quality assurance in Finland and abroad. A long-term mem-
ber of evaluation commissions within the Quality Assurance Agency in
Higher Education (qaa) underlined in the description of her experience
(Broady-Preston 2002, 3) that 'the training emphasized the importance
of teamwork rather than individual effort. For some academics, the pro-
cess of open and free sharing of information was a difficult one to grasp'.
The experience of the Inspectorates of Education in Europe, while
partly consistent in its content with the work of quality assurance agen-
cies, shows a multitude of models and criteria used for inspector recruit-
ing and training among different European countries (Standaert 2000,
34-6): past experience in education, interviews, trainings, written tests
on various themes, in-service training, 'in-house' refresher training, su-
pervision of a mentor, tutor support, closely observation, etc. Generally
speaking, a limited number of countries do have a well-defined approach
to beginner staff training, but little attention is paid to the systematic,
continuous on-the-job training of inspectors.
quality assurance in Slovenian higher education
Quality assurance in Slovenian heis has two branches: accreditation
and evaluation. Accreditation is needed before new heis are founded
or new educational programmes are offered, and is renewed every seven
years. Complying with amendments to the 2004 Higher Education Act
(zvis-d), article 80, these subsequent accreditation processes take into
account the findings of institutional self-evaluation and external evalu-
ation reports. These requirements additionally encouraged the he area
to establish a national system of quality assurance, especially systemically
coordinated iees, which would provide the heis with the desired com-
parability and credibility within Europe.
Consequently, in 2004, national Criteria for monitoring, assessment
and assurance of quality in the higher education institutions, study pro-
grammes, science and research, artistic and professional work (the Cri-
teria) were adopted (Merila za spremljanje [...] 2004). The Criteria were
based upon standards prescribed by enqa, eua and unesco. The other
important landmark was the Act Amending the Higher Education Act
(zvis - e) of September 2006, which grants the Council for Higher Educa-
tion of the Republic of Slovenia independence, hereby meeting the nec-
essary condition for the Council's independent and unbiased decision-
making in the processes of assessing and assuring quality.
The Criteria only represent the initial stage in introducing a compara-
ble quality assurance system. In 2006, the Slovenian national commission
for quality in higher education (ncqhe) was entrusted with elaborating
iee procedures and testing the Criteria in practice. Due to this inten-
tion, the ncqhe had to perform a pilot iees. The project was financed
by the Ministry of higher education, science and technology. Once the
testing phase is over, the iee procedure will be paid for by the hei itself,
or rather - the iee will constitute the hei's investment. By investing in
the culture of quality now, the hei expects to limit the cost for quality
assurance in the future (Wagenaar 2006).
Observing Visits at Evaluated Higher Education Institutions
The enqa's guidelines (2007, 25) suggest that an institution, providing
quality assurance structures, has in place internal quality assurance pro-
cedures which include an internal feedback mechanism, an internal re-
flection mechanism and an external feedback mechanism in order to
inform and underpin its own development and improvement. The re-
search group (hereinafter referred to as 'we') followed this recommenda-
tion to gather the responses on eec work quality, as is apparent from the
empirical section of this article.
description of the methodology
Due to the need for an all-encompassing research approach, where the
researcher not only obtains the answers to his/her questions, but can also
fully grasp the existing social situation and get a full picture of the group,
organization, or relationship (Flere 2000, 81), we opted for the system-
atic observation of visits to the evaluated heis. Standaert (2000,49) sug-
gests that the 'real [quality] assessment can only be done by observing
the inspector at work'. Since site-visits are a key part of the iee process,
we decided to observe the entire course of site-visits at individual heis,
which meant a two-day observation of on-site activity. All four heis,
participating in the pilot iee project, were included in the observation
processes and are hereinafter referred to as individual study cases.
We undertook - by means of independent, in-depth insight into the
activities of the iee participants in the pilot project - the task of identi-
fying the fields, stages or tools to be improved and proposing corrective
measures. This article, however, focuses only on one part of the findings,
relating to the professional competences of eec members. This research
is an evaluation case study, using a qualitative research approach.
The observation plan was laid out in advance. An observation check-
list was drawn up as the most important coordination tool the four ob-
servers had. It was previously submitted for peer review to all the partic-
ipating observers. During the eec training workshop, where a method-
ological approach to hei visits was simulated, one observer was also
tasked with verifying the applicability of the observation checklist.
Observers were independent in the sense that they have not, either
previously or at the time of the study, been involved in the pilot project
or any other forms of the ncqhe activities. Our trust in their profes-
sionalism was based on their current research work in the field of he,
quality or evaluation. Immediately before the beginning of the study, all
observers had been studying research methodology and they took part in
the eec training. The observation included four observers, so as to min-
imize the influence of the potential shortcomings of observers' personal-
ities (Milic 1965, 382-6, in Flere 2000) and thus analyzed all study cases,
which, in three instances, took place simultaneously. The observers were
separated from the groups that were evaluated and watched the process
without partaking in the interviews.
The evaluated hei were notified in advance about the observers' atten-
dance. Moreover, the attendance and role of the observers were likewise
confirmed by the ncqhe. The eec or the observers themselves intro-
duced the observer and explained their role to each of the interviewed
hei groups.
Observers were informed in advance about the subject observed, the
particular observation process and the key rules of unobtrusive obser-
vation. Even so, some situations arose during the observation process
(especially outside the interviews) that saw the observer transform from
unobtrusive to participant, as the participants would often perceive their
observer as a 'social stranger' (Flere 2000, 89) and initiate interaction
with him.
With the help of their laptop computers, our observers would record
their impressions and participant statements or added them by hand to
their observation checklists intended for that particular interview. Af-
ter the observation was complete, each observer wrote an observation
report. The observation coordinator qualitatively processed the reports
they had received. The analyses results were then presented to all at the
final ncqha project meeting.
During the final meeting of the ncqhe pilot project, one more survey
was carried out (in addition to observation) among the representatives
of evaluated heis and eec members that attended. The survey was con-
ducted through two survey questionnaires, one for the eec members
and the other for the representatives of heis. All persons meeting a pre-
requisite condition were included (i. e. the presence of eec members or
hei representatives at site-visits). Of 12 eec members present, 11 respon-
dents answered the questionnaire. Additionally, all 7 hei representatives
present answered the survey.
At the final meeting, the eec Chairs and the representatives of evalu-
ated heis were also given an opportunity to explain their own sugges-
tions and observations regarding the methodology of pilot iee to all
present. By the triangulation of these methods (observation, question-
naire survey and experience presentations) we tried to ensure a correct
understanding of the conclusions made by observers and to sustain the
findings.
research limitations
Observation is based on a more or less subjective appreciation of the
situation, which is also the main limitation of the research. Although the
feasibility of unobtrusive observation may be questioned, as the observed
individuals may differ in their attitude towards the observer's presence
and consequently react to it, this type of observer role ensures the highest
possible credibility of all the different observation methods (Flere 2000,
88).
A further limitation to observation lies in the fact that these research
techniques can provide an extremely detailed image of what is going
on and how long it has been happening, but they do not enable an all-
embracing description as to why things happen (Easterby-Smith, Thorpe
and Lowe 2005,144). Thus it is necessary to analyze these perceptions, in
other words: 'What is recorded must be meaningfully processed in ac-
cordance with our understanding of the observation focus' (Flere 2000,
81). The answers to W-questions were hence obtained through surveys
and with the help of participants, presenting their experience at the final
meeting.
Empirical Findings
The eec members used the interview as their main method of data col-
lection during their visits at heis. This method 'requires a high level of
skill in the interviewer, who needs to be knowledgeable about the inter-
view topic and familiar with the methodological options available, as well
as have a grasp of the conceptual issues of producing knowledge through
conversation. Interview research is a craft that, if well carried out, can
become art' (Kvale 1996,13).
Observers' findings, such as 'emphasis on personal opinion and expe-
rience . . . expressing own notions, opinions and views . . . transition to a
friendly approach . . . providing advice and instruction . . . the presence
of judgments and instant suggestions . . . patronizing . . . a very inquis-
itive approach of the commission', all mark the professionalism of the
eec work. In one studied case, 'the group interviews transformed into
plenary sessions which, quite often - in terms of their content - disre-
garded the evaluation questions'. This begs the question of what could
be regarded as an optimal methodological approach of the interviewer,
and results in the following, largely incompatible, hypothetical possibili-
ties. The first possibility is for the eec to empathize with hei representa-
tives, as this is likely to encourage trust and a greater openness from hei
representatives in sharing information. However, the levels of objectiv-
ity, dispassion and suggestiveness in the communication on the part of
eec members are questionable. The second possibility builds on the pre-
supposition that the eec must act dispassionately, thus distancing them-
selves from the representatives of evaluated heis, refrain from revealing
their own perspective, personal experience, etc. In this case, the eec can
expect a more pronounced reticence in communication from hei repre-
sentatives.
Based on their experience, enqa (2007,12) also presented some fairly
substantial discrepancies as to what ought to be the appropriate rela-
tionship ruling the interaction between heis and eec members. Some
professionals, mainly from agencies accrediting programmes or institu-
tions, take the view that external quality assurance is essentially a matter
of 'consumer protection', requiring a clear distance to be established be-
tween the quality assurance agency and the heis whose work they evalu-
ate. Meanwhile other agencies understand the main purpose of external
quality assurance to be the provision of advice and guidance in the pur-
suit of improving the standards and quality of study programmes and
related qualifications. The effort to establish a balance between account-
ability and improvement has emerged as a key responsibility of the eec
members.
In connection to the paragraphs above, the study case confirmed the
thoughts of Kvale (1996,101), who argues that an interaction among in-
terview participants leads to emotional statements about the topics being
discussed. This was due to the fact that 'the evaluator's role was too often
mistaken for that of a counsellor' and, in one case, 'a verbal argument
provoked a defensive reaction among faculty representatives - silence', in
other words, a communication hurdle with some of the hei representa-
tives. In spite of this, the e e c's work does comply with the e ua guidelines
(2005, 22), which emphasize that the eec 'does not judge the quality of
teaching and learning or that of research, nor does it rank or compare
one university against others' and that 'it should be emphasized that the
main preoccupation of the team is to be helpful and constructive rather
than threatening or punitive'.
Even the implementation of the questions asked by eec members that
were supposed to be mostly open-ended, was not optimal. 'Questions
were formulated too explicatively ... it started out fine, but there was
a lot of extra information, directing subsequent answers ... questions
were aimed at obtaining interviewees' opinions instead of the data that
would enable the evaluation of work within the hei ... questions were
suggestive, rooted in unfounded or vague conclusions and judgments'
were the findings made by three observers, potentially pointing out some
problems in acquiring unbiased information. One of the key character-
istics underlying open-ended questions, as carefully analyzed by Foddy
(1993,128), is that they should not imply answers. One of the observers,
however, did note that 'the questions [asked by eec members] did even-
tually become more structured and goal-oriented', which implies that
there were some self-evaluation or self-correction measures applied by
eec members in one study case.
The triangulation of methods as an approach combining several in-
dependent methods and measurements (Easterby-Smith, Thorpe, and
Lowe 2002,181) within the eec's work, proved optimal in two study cases
under observation. This can be concluded from the following feedback:
'the obtained information was verified in different target groups and
questions were asked about the issues that remained unclear from the
previously acquired materials' or 'the commission turned to their ques-
tionnaire and the documentation for support'. In two other instances, the
triangulation of methods was misinterpreted and implemented incor-
rectly. The interviewees were sometimes asked 'unnecessary questions,
where answers were already apparent from the questionnaire' and 'ques-
tions seeking opinions of faculty representatives, instead of information,
which would enable work evaluation', or provided 'information or drew
conclusions from the interviews that had not been validated [in other
evaluated groups]'.
Standaert (2000,47) argues that less attention should be paid to mem-
bers' familiarity with legislation, as this is already sufficiently represented
in administration. But the observation findings indicate a lower credi-
bility of those eec members (coming from different fields of activity)
who displayed only superficial knowledge of the legislation regulating he
or hei work in general. The survey questionnaire dealt with this study
topic. The eec members rated the claim that 'commission members had
adequate knowledge of higher education' with an average of 4.18, while
the representatives of heis gave it the average rating of 4.14 (on a scale of
1 to 5, with 5 being 'agree completely' and 1 being 'completely disagree'; all
average values mentioned in this article represent the regular arithmetic
mean). Despite the satisfactory (more or less rational) results that the
iee participants gave, the observers marked the spontaneous responses
on weak legislative knowledge as critical (the hei representatives were
astonished when they identified the gap, as was noticed also from their
voice sound).
In the survey questionnaire for eec members, the statement that
'commission members had adequate qualifications to carry out external
evaluation' received an average of 3.82 (on a scale of 1 to 5, with 5 being
'agree completely' and 1 'completely disagree'), while this same claim,
using the same scale, received an average grade of 4.71 from the rep-
resentatives of evaluated heis. In the presentations at the ncqhe final
meeting the necessity to upgrade the evaluators' competences was also
pointed out more by the eec members (3 statements), than by the hei
representatives (just 1 statement regarding the methodology in general).
In response to the following questions 'In your opinion, should there
be additional topics included in the training of eec members? If "yes",
which?' all 11 eec members who answered the survey questionnaire, re-
sponded affirmatively. Their suggestions mostly mirror the need to learn
about the examples of good iee practice in the he systems of other
countries, and for more research methodology training. These trainings
should be organized as workshops, ranging from interactive participants'
work to partial simulations of the iee process.
Implications
Due to this pilot iees, the Slovene he system took a historic step toward
in establishing a national quality assurance system. While the main fo-
cus of these pilot iees was on testing of the evaluation tools, the quality
of other, accompanying elements defining iee success, i.e. the profes-
sional competences of eec members, stayed in the background. This is
the main conclusion. The presented study provides a starting point for
self-improvement and proposes some guidelines for future measures at
the national level.
The most crucial among these is the introduction of more intensive
eec members training, as emphasized by the eec members. It seems
sensible that the qaa's evaluators training programme, which focuses
mainly on research methodology (2006, 19-20), should be expanded
with an educational module on higher-education legislation. Also it is
necessary that a Code of ethics is formed and considered by the iee
participants. The document ought to emphasize the preferred values
and principles of the eec members' work, hereby facilitating the cor-
rect choice of approach towards establishing interaction with the eval-
uated hei. Including the iee Manual, Code of ethics, national higher-
education legislation and research methodology the framework for the
eecs' training is formulated. At this stage it would be indispensable to
determine at national level, which body would be responsible for eecs'
trainings. The next precondition for acquiring quality eec members is
to define the necessary qualifications or criteria for appointing members,
besides the necessary training. It is indispensable to link these activities
to the potential national act, which would define the body (and its for-
mation) that will grant the licences and assess the quality of eec mem-
bers. This body would be responsible for creating and developing the
National evaluators register. In this way the evaluators' mobility would
be stimulated and the necessary quantity of evaluators would be easier to
provide.
The main limitation for now is the non affiliation of the Slovenian
evaluation body in the enqa and eqar that is enabled in the summer
2008 (see http://www.eqar.eu/). Negative impact is expected not just on
the evaluators' mobility and knowledge exchange, but on the develop-
ment, improvement and comparability of the iees as well. In spite of
granting the Council for higher education of the Republic of Slovenia
independence (zvis-e 2006), the necessary conditions that would allow
for affiliation in the enqa and eqar are not fulfilled.
Finally, we wish to emphasize the need for a systematically organized
feedback on the eec's work. This could become an important source
of information for self-improvement that could require just a minimal
financial input. Beside this internal feedback mechanism, it would be
worth supporting future research that would be focused on the contri-
butions of the foreign evaluators to the quality of the eecs (in the pilot
project the eec was formed just of the national subjects), looking both
from the eec members' perspective and from the perspective of the eval-
uated hei representatives.
The interesting further research that we propose is the accordance of
the objectives, regarding the hei quality, comparing the eec - external
- view on hei quality and the hei Quality commission - internal - view
on hei quality. We have to emphasize that the evaluations main target is
not just assuring accountability toward the stakeholders, but mainly to
sustain the hei's improvement.
This paper presents a unique methodology approach in setting up the
external feedback mechanism in the iee; complementing the observa-
tion with the questionnaire survey and the experience presentations. The
need of a many-sided view on the ecc work contributes to the reliability
of the findings.
Acronyms
eec - External Evaluation Commission
enqa - European Association for Quality Assurance in Higher Education
eua - European University Association
he - Higher Education
hei(s) - Higher Education Institution(s)
iee(s) - Institutional External Evaluation(s)
Criteria - Criteria for monitoring, assessment and assurance of quality
in the higher education institutions, study programmes, science and
research, artistic and professional work
ncqhe - National Commission for Quality in Higher Education
qaa - Quality Assurance Agency in Higher Education
unesco - United Nations Educational, Scientific and Cultural Organiza-
tion
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An Empirical Analysis of Credit Risk Factors
ofthe Slovenian Banking System
Boštjan Aver
The study presents the results of an analysis of credit risk factors of
the Slovenian banking system. The objective of the empirical analysis
is to establish which macroeconomic factors influence the systematic
credit risk of the Slovenian banking loan portfolio. The research results
have confirmed the main hypothesis that certain macroeconomic fac-
tors have a major influence on the examined credit risk. We could con-
clude that the credit risk of the loan portfolio depends on the employ-
ment or unemployment rate in Slovenia, on short and long-term inter-
est rates of Slovenian banks and the Bank of Slovenia, and on the value
of the Slovenian stock exchange index. We cannot claim that the exam-
ined credit risk depends on the inflation rate in Slovenia, the growth
of gdp (industrial production), eur and usd exchange rates or the
growth of Slovenian import and export.
Key Words: Slovenian banking system, credit risk factors,
loan portfolio, Bank of Slovenia, macroeconomic factors
jel Classification: G11, G21
Introduction
Empirical studies on credit risk factors have shown that the factors that
influence risk in all sorts of investments and cause credit risk are mainly
different macroeconomic factors (Saunders 1997; Crouhy, Galai, and Mark
2000). Changes in economic policies, political changes and the goals of
leading political parties also influence the range of the investment credit
risk (Saunders 1997; Belkin, Suchower, and Forest 1998). Since these fac-
tors are difficult to examine, there is no point in including them in 'the
research model'. Our objective is therefore to establish which macroeco-
nomic factors influence the systematic credit risk of the Slovenian bank-
ing system.
Foreign researchers, such as Saunders (1997), Asarnow (1996), Crouhy,
Galai, and Mark (2000), Carty and Lieberman (1996), as well as Carty
and Fons (1993), have determined that the range of the credit risk of an
individual investment (e. g. loan) is influenced by risk factors, which can
Dr Boštjan Aver is President ofthe Management Board
of Vzajemna, d. v. z., Slovenia.
be divided into those influencing the risk of all investments and caus-
ing investment's systematic credit risk and those influencing the risk of
an individual investment and causing investment's unsystematic credit
risk. The factors influencing the systematic credit risk are macroeconomic
factors, changes in economic policies, political changes and the goals
of leading political parties. Macroeconomic factors include the inflation
rate, the employment rate, growth in gross domestic product, stock in-
dex and exchange rate movements, and conjuncture fluctuations in the
economy. Changes in economic policies are represented by changes in
monetary and tax policies, economic legislation changes, as well as im-
port restrictions and export stimulation (Saunders 1997; Temeljotov Salaj
2005) L The factors influencing the unsystematic credit risk are primarily
the factors of individual customers, such as their personality, their finan-
cial solvency and capital, credit insurance and general terms and condi-
tions. In the case of companies, specific factors of the industry sector and
the company are emphasised (Mramor 1996). Industry factors include
the structure and economic successfulness of the industry, maturity of
the industry and its stability, while company factors include factors such
as general characteristics of the company, management, financial posi-
tion, sources of funds and financial reporting.
When deciding about new bank customers it is thus important to con-
sider the credit risk of an individual customer, which can nowadays be
measured with the help of various modern credit models (Aver 2003).
However, in assessing the influence of an individual obligor on the bank
operation risk and the influence of capital requirements, the risk of the
entire bank portfolio (see Asarnow 1996) must also be taken into ac-
count. Customer risk and portfolio risk are specified by the expected
loss and standard deviation of the loss, which defines the unexpected
loss (Ong 2000). The latter is often assessed as a multiple of the standard
deviation of the loss (Bessis 1998).
According to CreditMetrics™ technical document (jp Morgan 1997),
company credit risk may also be influenced by systematic market risk,
which is reflected in the changes in interest rates, stock index, exchange
rates and unemployment level. The probability of default of a company
and the probability of its credit rating migration is thus related to market
risk. In an ideal world, the methodological tool for bank risk measure-
ment should therefore link market risk and credit risk, which banks are
exposed to in their operations.
Figure 1 presents the influence of the state of world economy on default
65 ^ Number of defaulted companies
15 20-
16
m
<
						
						
17				28		
27	30	29			21 14	17 14
16					7	
1985 1986 1987 1988 1989 1990 1981 1992 1993 1994 1995 1996 1997
figure 1 Number of defaulted companies and the amount of defaulted loans
(adapted from Crouhy, Galai, and Mark 2000, 319)
62
12
8
4
0
probabilities of companies or frequencies of default of foreign compa-
nies in the period from 1985 to 1997. In 1990 and 1991, when the world
economy was in recession, the frequency of company defaults rose sub-
stantially. On the other hand, during the period after 1991, which was
characterised by a growing economy, the number of defaults declined.
Actual default probabilities of companies vary over time, depending
on the state of economy. In 1996, Carty and Lieberman conducted a study
on average probabilities of default and their standard deviations for in-
dividual credit rating categories of bank obligors in the period from 1970
to 1995, the results of which are provided in table 1. Similar studies have
been conducted by Carty and Fons (1993) and Lucas and Lonski (1992).
Tom Wilson and McKinsey and Company (Paul-Choudhury 1998) de-
veloped CreditPortfolioView, a multifactorial model for credit risk mea-
surement, which can be useful for generating the distribution of default
probabilities and credit rating migration probabilities for different in-
dustry sectors and for each individual country. CreditPortfolioView takes
into account that default probabilities and probabilities of obligor credit
rating migrations depend on the state of the economy. When the econ-
omy is not doing well, the probability of default of companies and credit
downgrades increase, and vice versa when the economy is strong (Belkin,
Suchower, and Forest 1998). Slower economic growth thus causes more
frequent credit rating migrations, with lower credit rating classes having
table 1 One-year default probabilities in the period from 1970 to 1995
Credit rating category	One-year default probability	
	Average	Standard deviation
aaa	0.00%	0.00%
aa	0.02%	0.12%
a	0.01%	0.05%
baa	0.15%	0.30%
ba	1.22%	1.35%
b	6.32%	4.78%
note Adapted from Moody's Investor Service 1995.
figure 2 Markov transition matrix
Initial rating
Rating at year-end
aaa aa
a bbb bb
ccc Def.
aaa
aa
a
bbb
bb
b
ccc
Recession
Expansion
note Adapted from Wilson 1997.
a higher correlation with macroeconomic factors. Credit rating migra-
tions during the time of economic recession and expansion can be clearly
illustrated with the Markov transition matrix, as shown in figure 2.
As economic growth depends on macroeconomic factors, CreditPort-
folioView takes into account the links between macroeconomic variables
and default and credit rating migration probabilities of companies. The
model includes the unemployment rate, gross domestic product growth,
the level of long-term interest rates, foreign exchange rates, government
expenditures and the savings rate among macroeconomic factors (Belkin,
Suchower, and Forest 1998).
During a period of recession, default probabilities of speculative-grade
obligors are higher than their average default probability, as frequencies
of obligor migrations to lower credit rating categories are increasing and
frequencies of upward obligor migrations are decreasing. The contrary
b
holds true for the period of economic expansion. The probabilities of
obligor credit rating migrations thus depend on the credit cycle, the in-
dustry sector and the country, the credit cycle, however, being the most
important factor in the variability of company default probabilities (Ban-
gia, et al. 2002).
The Duffie and Singleton model has been generalised by Lando (1997)
and Jarrow and Turnbull (1998) with an assumption that the intensity
of default or the obligor default probability (A) depends on the vector
of various macroeconomic variables (Xt), such as risk-free interest rate,
stock index, etc. The obligor default probability is modelled in the form
of the Cox distribution, which has the characteristics of the Poisson dis-
tribution and depends on the vector of macroeconomic variables (Xt):
m = Ä(xt).	(1)
In 1997, Duffie and Singleton derived a risk-adjusted short-term inter-
est rate (7):
7 (t) = r(t) + A(t)LGD + 1.	(2)
In order to simplify the model implementation, Jarrow and Turnbull
suggested the following:
1.	the risk-free interest rate r(t) is in accordance with the one-factor
Vasicek model from 1977,
2.	the obligor default probability A(t) depends on the change in the
risk-free interest rate r(t) and unexpected changes in the value of
the market index WM(t):
A(t) = Äo + Air(t) + hWM (t),	(3)
where A0, A1 and A2 are constants. WM(t) indicates unexpected changes
in the value of the market index according to the value of the stock index
M(t), for which standard lognormal distribution is presupposed:
dM(t) = [r(t)dt + aMdWM (t)]M(t).	(4)
1.	the level of loss given default (lgd) is constant,
2.	the liquidity premium (l) depends on the risk-free interest rate r(t),
the stock index M(t) and the variability of the daily value of the
stock index:
l(t) = lo + lir(t) + hM(t) + 13[Mh(t) - ML(t)]2,	(5)
where M (t) is the stock index value on a certain day, while MH (t) and
ML(t) indicate its highest and lowest daily value.
In the following sections, we explain the underlying conceptual and
methodological framework and the results of the analysis conducted ac-
cording to foreign findings on the example of the Slovenian banking sec-
tor.
Developing the Hypotheses
On the basis of conducted studies and analyses, Saunders (1997), Asar-
now (1996), Crouhy, Galai, Mark (2000), Carty and Lieberman (1996), as
well as Carty and Fons (1993) have determined that the range of the credit
risk of an individual investment is influenced by risk factors which influ-
ence the systematic credit risk and risk factors which influence the unsys-
tematic credit risk of an investment. The factors influencing the system-
atic credit risk are macroeconomic factors, changes in economic policies,
political changes and the goals of leading political parties. Macroeco-
nomic factors include primarily the inflation rate, the employment rate,
growth in gross domestic product, stock index and exchange rate move-
ments, and conjuncture fluctuations in the economy. Changes in eco-
nomic policies are represented by changes in monetary and tax policies,
economic legislation changes, as well as import restrictions and export
stimulation (Saunders 1997; Mramor 1996).
Based on the above-mentioned findings of foreign researchers and ex-
perts, particularly in terms of the macroeconomic factors which can in-
fluence the range of systematic credit risk, the main hypothesis can be
developed:
H1 The range of the credit risk of the Slovenian banking system depends
on specific macroeconomic factors.
The main hypothesis can be further divided into several subhypotheses.
The results of testing the latter represent the basis of confirming or re-
jecting the main hypothesis (h1). In order to confirm or reject individ-
ual subhypotheses, it is necessary to determine the influence of specific
macroeconomic variables on the credit risk of our banking system. The
subhypotheses (H1-1 to H1-7) and examined variables for testing them
are as follows:
H1-1 The systematic credit risk of the Slovenian banking system depends
on the inflation rate in Slovenia. Examined variables: revalor,
czp and dpc.
H1 - 2 The systematic credit risk of the Slovenian banking system depends
on the employment or unemployment rate in Slovenia. Examined
variables: zaposl and brezposl.
h1-3 The systematic credit risk of the Slovenian banking system de-
pends on the growth in Slovenian gross domestic product. Exam-
ined variable: rastindu.
H1-4 The systematic credit risk of the Slovenian banking system de-
pends on the short-term and long-term interest rate activities of
banks and the Bank of Slovenia. Examined variables: medbom,
lombom, zom, omtekpo, omstan, omos, ompotr, nom-
obs, nompotr, nomos, nomstan, nomdobs and nomdos.
H1-5 The systematic credit risk of the Slovenian banking system depends
on the movement of specific exchange rates. Examined variables:
eur and usd.
H1-6 The systematic credit risk of the Slovenian banking system depends
on the movement of the value of the Slovenian stock exchange in-
dex (sbi) or the movement of share trade on the organised securi-
ties market. Examined variables: sbi and promdeln.
H1-7 The systematic credit risk of the Slovenian banking system depends
on the Slovenian export and import growth. Examined variables:
izvoz and uvoz.
In examining the influence of 25 chosen macroeconomic variables on
the Slovenian banking system credit risk, factorial analysis is employed
in order to find new dimensions of macroeconomic factors, which rep-
resent common characteristics of some macroeconomic variables. Thus,
the second hypothesis can be developed:
H2 The method of principal components and choice of two factors will
provide us with good enough partial correlation coefficients between
individual macroeconomic variables and both factors, so that the first
factor and second factor will together account for more than 50% of
the overall credit risk variance.
Thus, the used empirical analysis of the credit risk factors of the Slove-
nian banking system portfolio is based on monthly data of 25 chosen
macroeconomic factors and the calculated indicator of the Slovenian
banking system portfolio credit risk for the period from 31 December
1995 to 30 November 2002. The evaluation of the initial hypothesis (h1):
The range of the credit risk of the Slovenian banking system depends on
specific macroeconomic factors, was based on the set research model ex-
plained below.
Research Methodology
data collection and operationalisation
of the variables
According to the findings of foreign researchers such as Saunders (1997),
Crouhy, Galai, and Mark (2000), Belkin, Suchower, and Forest, (1998),
Carty and Lieberman (1996), as well as Carty and Fons (1993), the data
on potential factors that influence systematic credit risk could be found
among different macroeconomic variables, e. g. inflation rate, employ-
ment or unemployment rate, the increase in gross domestic product (in-
dustrial production), the movement of short and long term interest rates
of banks and the central bank, the movement of exchange rates (e. g. eur
and usd), the movement of the value of the stock exchange index and
share trade, and other macroeconomic factors (e. g. import and export).
Thus, 25 different macroeconomic factors were chosen to test the stated
hypothesis.
The research model design is shown in figure 3. The figure presents cho-
sen factors which can influence the systematic credit risk and which rep-
resent the entry model variables used to determine which of the chosen
25 macroeconomic variables have a significant influence on the range of
the Slovenian banking system credit risk.
The following data sources were used in the analysis:
•	gvin - Gospodarski vestnik database,
•	sors - Statistical Office of the Republic of Slovenia database,
•	Bank of Slovenia database (the archive of financial data from the
Bulletins of the Bank of Slovenia),
•	monthly balance sheets and profit and loss accounts of all Slovenian
banks for the period from 31 December 1995 to 30 November 2002,
•	other publicly available materials of the Bank of Slovenia.
The conducted analysis of the Slovenian banking system portfolio is a
quantitative analysis, since the attributes used to describe the systematic
credit risk factors of the banking portfolio are numerical, not descrip-
tive. The influence of specific macroeconomic factors on the range of the
Slovenian banking system credit risk has been examined with the help
of spss software and specific statistical methods, such as multiple linear
regression and factorial analysis.
An Empirical Analysis of Credit Risk Factors 325
figure 3 Systematic credit risk factors
Inflation rate	revalor	Revalorisation clause
	czp	Consumer goods prices*
	dpc	Retail prices
Employment	zaposl	Employment rate
	brezposl	Unemployment rate
gdp	rastindu	Industrial production
Interest rates	medbom	Interbank ir
	lombom	Lombard ir
	zom	Default ir
	omtekpo	Real ir on current business loans*
	omstan	Real ir on long-term home loans
	omos	Real ir on long-term fixed asset loans
	ompotr	Real ir on short-term consumption loans
	nomobs	Nominal ir on short-term current asset loans*
	nompotr	Nominal ir on short-term consumption loans
	nomos	Nominal ir on long-term fixed asset loans
	nomstan	Nominal ir on long-term home loans*
	nomdobs	Nominal ir on short-term foreign exchange loans for current assets
	nomdos	Nominal ir on long-term foreign exchange loans for fixed assets
Exchange	eur	eur exchange rate
rates	USD	USD exchange rate*
Stock exchan-	sbi	Slovenian stock exchange index
ge market	promdeln	Stock exch. trade and the trade on the free share market*
Export/	izvoz	Import of goods and services
import	uvoz	Export ofgoods and services
* Systematic credit risk.
the sample
The established model is based on the data of the variety of macroe-
conomic credit risk factors, collected on monthly basis, as well as on
monthly data of the calculated indicator of the Slovenian banking system
portfolio credit risk for the period from 31 December 1995 to 30 Novem-
ber 2002. The chosen assessment period of the Slovenian banking system
after 1995 is the most appropriate one for the analysis, since the period
before the end of the year 1995 would mystify the results of the analysis
to a great extent.
In that period, the range of the Slovenian bank portfolio credit risk
was being contracted due to different system effects, among others, due
to the implementation of the rehabilitation programme in Ljubljanska
banka, d. d. and Kreditna banka Maribor, d. d. (in 1993), as well as in
lb Komercialna banka Nova Gorica, d. d. (in 1994). System measures are
also visible in the period from March 1994 to September 1996, namely in
the exchange of poor receivables with government bonds.
Taking into account the examined period from 31 December 1995 to
30 November 2002, when there were no significant system effects on the
range of the Slovenian bank credit risk, good results can be obtained on
the basis of a statistical analysis of the effect of macroeconomic factors
on the credit risk of the Slovenian banking system portfolio. At the same
time, monthly data for all independent variables and dependant variables
can be examined as individual cases and not as time series, thus neglect-
ing the time influence or the time components on the credit risk range.
The indicator of loan portfolio credit quality was first defined as a ratio
between the range of formed value corrections for credit risk of loans to
the non-banking sector2 (government excluded) and the range of gross
loans given to the non-banking sector (government excluded). The loans
given to the government were excluded. According to the methodology
of the Bank of Slovenia, the loans given to the government are included
in the loans given to the non-banking sector. Therefore, they influence
or have greater effect on the percentage of the formed value corrections,
since receivables to the government had better ratings. We analysed the
mentioned indicator of the credit quality of the Slovenian banking sys-
tem loan portfolio in the period from December 1995 to November 2002.
data analyses
We can establish which of the 25 chosen macroeconomic variables influ-
ence the range of the Slovenian banking system portfolio credit risk with
the method of multilinear regression. The following indicator (tveg1)
was defined for measuring the credit risk:
Value corrections for the credit risk of loans given to the non-banking sec-
tor/gross loans to the non-banking sector.
The multiple linear regression model can also be presented as (see fig-
ure 3):
An Empirical Analysis of Credit Risk Factors 327
tveg1 = ß1 + ß2 revalor + ß3 czp + ß4 dcp + ß5 zaposl
+ ß6 brezposl + ß7 rastindu + ß8 medbom
+ ß9 lobom + ß10 zom + ß11 omtekpo + ß12 omstan
+ ß13 omos + ß14 ompotr + ß15 nomobs
+ ß16 nompotr + ß17 nomos + ß18 nomstan
+ ß19 NOMDOBS + ß20 Nomdos + ß21 EUR + ß22 USD
+ ß23 SBI + ß24 promdeln + ß25izvoz
+ ß26 uvoz + u	(6)
The analysis of the influence of the 25 chosen macroeconomic vari-
ables on the credit risk of the Slovenian banking system could also be
conducted with a factorial analysis (method of principal components), by
finding new dimensions of macroeconomic factors that represent com-
mon characteristics for some of the macroeconomic variables.
Findings and Results
results of multiple linear regression model
As a result, we get a multiple linear regression model of seven statistically
significant variables, with R-square being equal to 0.863. This means that
86.3% variability of the systematic credit risk of the Slovenian banking
system portfolio is explained by linear dependency on seven macroeco-
nomic factors. It can be noticed that the increase in the real interest rate
for short-term consumption loans, the increase in the Slovenian stock
exchange index, a decreasing number of employees in Slovenia, an in-
creasing securities interest rate (interest rate of the Bank of Slovenia for
securities exchange) and the increase in the real interest rate on home
loans have the greatest influence on the increase in credit risk. In a way,
the mentioned results are logical. On the other hand, the two results, the
increase in the real interest rate for long-term loans for current assets and
the increase in the inter-banking interest rate, which have an effect on the
decrease of the Slovenian banking system portfolio credit risk, seem less
logical.
The obtained results are shown in table 2 and figure 4. Table 2 shows
partial regression coefficients for all 7 macroeconomic factors which have
a significant influence on the range of the examined credit risk, as well
table 2 Partial regression coefficients and T-test for testing their influence
Variable	(1)	(2)	(3)	(4)	(5)	(6)
Constant	17.62368	1.981	-	8.898	0.000	-
ompotr	0.45794	0.068	2.902	6.775	0.000	0.614
sbi	0.00050	0.000	0.551	6.046	0.000	0.570
omos	-0.66577	0.119	-2.681	-5.577	0.000	-0.539
zaposl	-0.00001	0.000	-0.463	-4.441	0.000	-0.454
medbom	-0.05264	0.014	-0.350	-3.684	0.000	-0.389
lombom	0.09185	0.027	0.179	3.343	0.001	0.358
omstan	0.12914	0.066	0.600	1.949	0.050	0.218
notes Column headings are as follows: (1) unstandardized coefficients: B, (2) stan-
dard error, (3) standardized coefficients: ß, (4) T-test: t, (5) significance, (6) partial cor-
relation coefficients.
Systematic credit risk
of the Slovenian banking
system
figure 4 Dependence of the Slovenian banking system credit risk on seven
macroeconomic variables on the basis of partial correlation coefficients (r)
and partial regression coefficients (ß); R2 = 0.863
as T-test results in relation to testing the characteristics of the indepen-
dent variables' influence on the credit risk. Moreover, figure 4 illustrates
an extended linear regression model, which fully explains the variabil-
ity of the systematic credit risk range of the Slovenian banking system
portfolio.
results of factorial analysis - method of principal
components
In examining the influence of 25 chosen macroeconomic variables on the
Slovenian banking system credit risk, factorial analysis was employed in
order to find new dimensions of macroeconomic factors that represent
common characteristics for some of the macroeconomic variables.
The method of principal components and choice of two factors pro-
vided us with partial correlation coefficients between individual macroe-
conomic variables and both factors. The first factor (various interest rates
of the Slovenian banking system) explains 58.8% of the credit risk vari-
ance, while the second factor (other macroeconomic factors) explains
10.8%. Thus, the entire model explains approximately 69.6% of the over-
all credit risk variance. The rest of the variance can be ascribed to specific
impacts that are not included in the model. The obtained result is rela-
tively good, although it should be borne in mind that there exist other
credit risk factors besides the examined ones.
The obtained communalities for individual macroeconomic variables
show what extent of the individual variable variance is accounted for by
both factors. Despite the fact that only the variables with a communal-
ity higher than 0.2 should be included in the analysis, the variables of
consumer goods prices (cpz) and retail prices (dpc) - their explained
variance with both factors being less than 20% - have also been included
in the analysis. The explained variance of the other 18 macroeconomic
variables with both factors is more than 20%. Therefore, they can be in-
cluded in the examined analysis without difficulty.
Thus, the results of the factorial analysis have shown that 20 macroe-
conomic variables are connected into 2 significant factors. The following
10 variables form the part of the first factor and they represent short and
long-term real and nominal interest rates:3 real interest rate on current
business loans, real interest rate on long-term current asset loans, real
interest rate on long-term home loans, real interest rate on short-term
home loans, nominal interest rate on short-term current asset loans,
nominal interest rate on short-term consumption loans, inter-bank in-
terest rate, nominal interest rate on long-term home loans, nominal in-
terest rate on long-term current asset loans and back interest rate.
The macroeconomic variables that form the second factor are the fol-
lowing: export of goods and services, import of goods and services, the
rate of industrial production, the number of all people employed in
Slovenia, retail prices, cost of goods sold, usd exchange rate, eur ex-
change rate, the value of the Slovenian stock exchange index, stock ex-
change trade and the trade on the free share market.
The first factor could be labelled as 'interest rates of the Slovenian
banking system', for it comprises different real and nominal (short and
long-term) interest rates of Slovenian banks. The second factor could be
labelled as 'other macroeconomic factors', for it comprises 10 different
macroeconomic variables, e. g. export (import) of goods and services,
employment rate, growth rate of production, exchange rate, develop-
ments on the securities market and the inflation rate in Slovenia.
It is possible to show the position of the above-average and below-
average risk of portfolios of our banking system regarding the first fac-
tor that represents the interest rates of the Slovenian banking system
and regarding the second factor representing other macroeconomic fac-
tors. The group of the 46 above-average risk portfolios of the Slovenian
banking system lie in the top right square,4 which means that the above-
average risk portfolios have high values of the first factor (high short and
long-term interest rates) and medium values of the second factor (va-
riety of macroeconomic factors). On the other hand, the below-average
risk portfolios of our banking system lie in the bottom left square,5 which
means that these portfolios have low values of short and long-term in-
terest rates and low values of the variety of macroeconomic factors. The
position of the above-average and below-average portfolios of our bank-
ing system regarding the first factor, which represents the interest rates
of the Slovenian banking system, and regarding the second factor, which
represents other macroeconomic factors, is shown by figure 5.
Conclusions and Implications
The results of the analysis of the credit risk of the Slovenian banking
system for the period from December 1995 to November 2002 show that
specific macroeconomic factors have important influence on the range of the
credit risk of the Slovenian banking system portfolio. The increase in credit
risk is highly influenced by the increase in short and long-term interest
rates of Slovenian banks, the increase in the value of the Slovenian stock
exchange index and the decrease in the number of employees.
In the examined period of 84 months, one of the most prominent fea-
tures of the portfolio has shown above-average risk in the period of high
real and nominal interest rates on different short and long-term loans,
during the period of high loan on securities interest rates and high inter-
	1 - above-average risk portfolios •
• 0 - below-average risk portfolios	
-1.0
-0.5
0.0	0.5
Interest rates of the banking system
1.0
figure 5 Position of the above-average and below-average portfolios of the Slovenian
banking system
bank interest rate, in the period of high unemployment (or low employ-
ment) and in the period of low eur and usd exchange rates. Taking into
account the theory of macroeconomic factors' influence on credit risk,
the given results are fairly logical and expected.
The examined macroeconomic variables are related by 2 characteristic
factors. The first is represented by the interest rate of the Slovenian bank-
ing system, which includes different real and nominal interest rates for
both short and long-term loans. The second can be described as the other
macroeconomic factors, as it consists of different macroeconomic vari-
ables. The high value of the first factor and the medium value of the
second factor is the most significant in the group of 46 above-average
risk portfolios of the banking system. On the other hand, the low value
of both factors is significant for the group of below-average risk of all
credit risk factors.
The analysis findings have shown that certain macroeconomic factors
have significant influence on the range of the Slovenian banking system
credit risk. Therefore, we can conclude that, among other factors, Asian
and Russian crises most probably influenced the increase of the range of
the loan portfolio credit risk in non-bank sectors in the period between
1997 and 1998. On the other hand, favourable movements of macroeco-
nomic government environment, the growth of consumption prior to
vat implementation, good business results in the real sector and growth
of the economies of the most important market partners influenced the
increase in the quality of credit portfolios of banking systems between
1999 and 2000. Thus, we can state with certainty that credit risk in Slove-
nian banks shows positive reactions and that banks have prompt reaction
time in cases of unfavourable market situation.
In spite of the analysis findings showing that macroeconomic factors
have substantial influence on the range of the credit risk in the Slovenian
banking system, we cannot overlook the influence of other factors that the
analysis does not explain - limitations of the research model. These are the
other factors that could have influenced credit risk in the banking system
in the examined period between December 1995 and November 2002:
•	the first case of bankruptcy in Slovenia: the bankruptcy of Komer-
cialna banka Triglav d. d. in 1996, due to compulsory liquidation,
•	stricter criteria for client categorisation since January 1997 (since
that date, the receivables insured by assets put in pledge could be
solely categorised into one group higher than the debtor's credit
rating);
•	the influence of the bank business results on provisions policy
(more conservative policy on estimating bank clients is typical for
the period of higher profits and preparation for new investments),
•	considerably higher net provisions for credit risk for the portfolio
of the bank skb d. d. in December 2001.
The results of the influence of the chosen macroeconomic factors on
the credit risk in the banking system have confirmed the main hypothesis
(h1), as certain macroeconomic factors have major influence on the ex-
amined credit risk. Further, we also confirmed some subhypotheses (H1-2,
H1-4 and H1-6), concluding that the credit risk portfolio of the Slove-
nian banking system depends on the employment or unemployment
rate in Slovenia (H1-2), on short and long-term interest rates activities
of Slovenian banks and the Bank of Slovenia (H1-4), and on the value
of the Slovenian stock exchange index (h1-6). Contrary to that, we can-
not claim that the examined credit risk depends on the inflation rate in
Slovenia (H1-1), the growth of gdp - industrial production (h1-3), eur
and usd exchange rates (H1-5) or the growth of Slovenian import and
export (H1-7).
On the basis of the obtained factorial analysis results we can also con-
firm the second hypothesis (h2) that more than 50% of the overall credit
risk variance of the examined Slovenian banking system portfolio can be
accounted for by the two factors together.
The conducted analysis and obtained results of the study of credit risk
factors in Slovenia can also be beneficial to banks in other countries in
transition, which do not yet apply the latest credit risk measurement and
management methods. Moreover, an important challenge is seen in recon-
ducting the study of the influence of macroeconomic factors on the range of
the credit risk of the Slovenian banking system portfolio with the data
which are yet to become available in the following years. It would also
be useful to undertake such studies in other countries, particularly in
the area of ex-Yugoslavia, where different findings about the influence
of individual macroeconomic factors on the credit risk can be expected
to arise due to a higher risk of their banking system portfolio and lower
economic development. The results of our analysis are also applicable
to other financial institutions such as insurance companies or pension
funds in risk managements of their financial investments. Finally, it will
be useful to elaborate the study of credit risk factors by employing new
and coming statistical tools such as structural equation modelling or
gravitational macroeconomic models, which can further increase the re-
liability of results.
Notes
1	In addition to the mentioned factors, the systematic credit risk of an
investment may also be influenced by the environment, e. g. the move-
ments of immovable property value, etc. (Temeljotov Salaj 2006).
2	Provisions and charges.
3	A high partial correlation coefficient associated with the first factor is
significant for the 10 variables mentioned.
4	The value of the first factor is 0.38 and the value of the second factor is
0.09.
5	The value of the first factor is -0.5 and the value of the second factor
is -0.12.
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2008
Management International Conference
Intercultural Dialogue
and Management
An International Conference
organised by the University of Primorska,
Faculty of Management Koper, Slovenia,
University Centre for Euro-Mediterranean
Studies, Slovenia, and the European
Institute of the Mediterranean, Spain
26-29 November 2008
Barcelona, Spain
Conference Aims
The Conference will focus on intercultural dialogue as the consequence of in-
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complexities, the knowledge as to how to manage these processes is still lim-
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of management and intercultural dialogue. Special attention will be given to the
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their contributions to a number of relevant referred international journals.
Conference Officers
Conference Chair: Dr. Egon Žižmond
General Chair: Dr. Binshan Lin
Program Chair: Dr. Janez Šušteršič
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Contact
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Managing Global Transitions
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VOLUME 6 • NUMBER 3 • FALL 2008 • ISSN I58I-63II
229 The Relationship Between Entrepreneurial Intensity
and Shareholder Value Creation
Pierre Erasmus
Retha Scheepers
257 The Relationships Among Leadership Styles, Entrepreneurial
Orientation, and Business Performance
Chung-Wen Yang
277 How Internal and External Sources of Knowledge Contribute
to Firms' Innovation Performance
Anja Cotič Svetina
Igor Prodan
301 Ensuring Professionalism of the External Evaluation Commission:
The Slovenian Case Study
Karmen Rodman
Nada Trunk Širca
317 An Empirical Analysis of Credit Risk Factors
of the Slovenian Banking System
Boštjan Aver
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