ISSN I581-63II
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Managing
Global
Boštjan Antončič
International
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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 and Montenegro
Terrice Bassler Koga, Open Society Institute,
Slovenia
Cene Bavec, University of Primorska,
Slovenia
Jani Beko, University of Maribor, Slovenia
Vito Bobek, University of Primorska,
Slovenia
Štefan Bojnec, University of Primorska,
Slovenia
Suzanne Catana, State University of New
York, Plattsburgh, usa
David Dibbon, Memorial University
of Newfoundland, Canada
Jeffrey Ford, The Ohio State University, usa
Tim Goddard, University of Calgary, Canada
Noel Gough, University of Canberra,
Australia
George Hickman, Memorial University
of Newfoundland, Canada
Robert D. Hisrich, The Garvin School of
International 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
Leonard H. Lynn, Case Western Reserve
University, usA
Monty Lynn, Abilene Christian University, usa
Neva Maher, Ministry of Labour, 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
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 of Ljubljana, Slovenia
Zvone Vodovnik, University of Ljubljana,
Slovenia
Manfred Weiss, Johan Wolfgang Goethe
University, Germany
Min-BongYoo, Sungkyunkwan University,
South Korea
Pavel Zgaga, University of Ljubljana, Slovenia
EDITORIAL OFFICE
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Text Design and Typesetting: Alen Ježovnik
Managing Global Transitions
International Research Journal
volume 3 • number 2 • fall 2005 • :ssn i58i-63ii
Table of Contents
115 The Editor's Corner
117 Corporate Ownership, Leadership and Job
Charateristics in Russian Enterprises
Moshe Banai
Jacob Weisberg
139 Joint Dynamics of Prices and Trading Volume
on the Polish Stock Market
Henryk Gurgul
Pawel Majdosz
Roland Mestel
157 Human Capital and Economic Growth
by Municipalities in Slovenia
Matjaž Novak
Štefan Bojnec
179 Where is the Border Between an Information System
and a Knowledge Management System?
Imandra Galandere-Zile
Viktorija Vinogradova
197 The Quality of Entrepreneurship Education
and the Intention to Continue Education:
Slovenia and Romania
Boštjan Antoncic
Cezar Scarlat
Barbara Hvalic Erzetic
The Editor's Corner
With this new issue, the journal enters a new phase of gaining interna-
tional recognition. The journal has been recently included in two inter-
national databases: the International Bibliography of the Social Sciences
(iBss) and the EconPapers database (the Research Papers in Economics
Database - RePEc). At this occasion I would like to thank the members
of the editorial team and all others who contributed with their activities
to the internationalization of the journal.
The journal continues focusing on the transition research and empha-
sizing its openness to different research areas, topics, and methods, as
well as the international and interdisciplinary research nature of schol-
arly articles published in the journal. The current issue covers topics such
as the impact of organizational ownership on the leadership and job
characteristics, the relationship between stock returns and trading vol-
ume, the nature of economic growth, the comparison between informa-
tion systems and knowledge management systems, and the relationship
between entrepreneurship education quality and continuation.
This issue starts with a paper on the application of the Western the-
ory of organization's ownership in Russia. The analyses of the authors
Moshe Banai and Jacob Weisberg suggest that ownership types influence
the leadership style and employees' jobs characteristics. In the second
paper, Henryk Gurgul, Pawei Majdosz, and Roland Mestel analyze the
relationship between stock returns and trading volume by using stock
data from Poland. In the third paper, Matjaž Novak and Štefan Bojnec
expose findings of the analysis of the nature of economic growth of the
Slovenian economy at the aggregate and at the municipality level. The
fourth paper of Imandra Galandere-Zile and Viktorija Vinogradova ex-
amines the border between information systems and knowledge man-
agement systems. In the last paper, Boštjan Antoncic, Cezar Scarlat, and
Barbara Hvalic Erzetic compare entrepreneurship education satisfaction
and quality between Slovenia and Romania and assess the relationship
between education quality and continuation in both countries.
Boštjan Antoncic
Editor
Corporate Ownership, Leadership and Job
Charateristics in Russian Enterprises
Moshe Banai
Jacob Weisberg
This study tests the application of the Western theory of organization's
ownership in Russia, suggesting that ownership types - such as state-
owned and private - influence leadership style and employees' jobs
characteristics. A sample of 724 Russian employees in 15 service and
manufacturing companies was surveyed. The results indicate that, con-
trary to Western theories, the leadership in Russian state-owned en-
terprises tends to be perceived as being more effective than the lead-
ership in private enterprises. Similarly, jobs in state-owned enterprises
are more enriched than in private companies. Explanations and impli-
cations are provided.
Key Words: leadership, job characteristics, state-owned enterprises,
private organizations, Russia
jel Classification: f, h, m
Introduction
ownership systems
This study tests the application of the Western theory of organization's
ownership in Russia. More specifically, it tests the relationship between
organizations' ownership, the perceived leadership style of their man-
agement, and the degree of job enrichment. The study extends the West-
ern research of similarities and differences between public and private
sector organizations (Allison 1979; Bozeman 1987; Buchanan 1974; 1975;
Chubb and Moe 1988; Coursey and Rainey 1990; Lawler 1981; Perry and
Porter 1982; Perry and Rainey 1988; Rainey 1979, 1983; Rainey, Backoff,
and Levine 1976, Solomon 1986) to Russia, a country that is different
Dr Moshe Banai is Professor of Management at the Zicklin School
of Business, Baruch College, usa.
Dr Jacob Weisberg is Professor of Management at the Graduate School
of Business Administration, Bar Ilan University, Israel.
The authors' names are presented in an alphabetical order indicating
equal contribution. The authors would like to thank two anonymous
reviewers for their valuable comments.
from the usa in its political, economic and social systems. Western stud-
ies that analysed organizational economic performance have found no
relationship between ownership and performance in the usa (Becker
and Potter 2002) or in other countries such as the Czech Republic (Ko-
cenda and Svejnar 2003), Greece or Portugal (Barbosa and Louri 2005).
Yet, the theory of ownership (Pierce and Rodgers 2004) has provided
continuous support to the argument that employees' ownership influ-
ences employees' perceptions and attitudes towards their organization,
and consequently their performance (Employee Ownership Foundation
2005). The question is to what extent an organization's ownership type
actually influences workers' attitudes towards their leadership and jobs in
the transitional economy of Russia. To answer this question we provide
a short history of Russia's ideological system and its present ownership
system.
Communism was the law of the state from the Bolshevik revolution
in 1917. Private property was outlawed and a centrally planned economy,
based on Lenin's vision of the Russian economic system as one large en-
terprise, was established. The Politburo was at the top of the national
vertical chain of command, while the individual worker was at the bot-
tom. Gosplan, the central planning committee, was the Politburo's eco-
nomic arm, which designed five-year plans for the entire nation. These
plans dictated what product would be produced in each plant, in what
quantities, and at what internal price. Industrial ministries oversaw the
execution of those five-year plans. Enterprise managers were personally
responsible for meeting the production plans. Managers expected their
subordinates to execute orders without questions in exchange for hous-
ing, health and day care, recreational centers and other fringe benefits.
As a direct result of Gorbachev's Glasnost (openness) and Perestroika
(change) policies, the centrally planned economic system collapsed. In
1991 the Soviet Union ceased to exist and since then the country has been
steadily shifting from its previous political and economic structure into
a more democratic and free market economy.
This new economic system is however still in its rudimentary stage.
Students who investigated the Russian transformation into a free market
economy have concluded that this transformation is less successful than
that of other former Soviet states (Goldman 1997; Shama 1995). Thus,
although the cultural difference between Russia and Western countries
may not be as great as one may expect, the distinction between a free
market economy and a transitional economy could be substantial.
Some theoreticians advised against the use of Western management
ideas in other nations (Hofstede 1980,1983; Spender 1993). Adler (1983,
1991) further contended that to assume that what was true for American
workers in the usa would also be true for workers in other countries was
wrong. Moreover, Western findings about the relationship between or-
ganizations' ownership type and performance are, at best, mixed. Hence,
we use theories of job characteristics rather than the theory of ownership
to hypothesize about a possible variance in leadership styles in Russian
state-owned and private companies. The next sections describe theories
of leadership and those of job characteristics.
leadership
This study investigates contingencies of leadership in state-owned and
private enterprises in Russia. It applies a variation on Fiedler's (1967,
1996), House and Mitchell's (1974) as well as Hersey and Blanchard's
(1993) interpretation of the contingencies of effective leadership and
Blake and Mouton's (1985) measure of leadership effectiveness. It uses job
characteristics of employees to evaluate the perceived leadership style of
management in state-owned and private enterprises in Russia. The dif-
ferences between perceived leadership styles of managers in state-owned
enterprises and in private enterprises in Russia could be delineated along
three main issues: participation of employees in decision-making, man-
agerial abilities, and employees' incentives. The leadership style of Rus-
sian managers seems to be changing from a centrally controlled to a mar-
ket oriented style. If in the past power was centralized, today managers
are trying to shift some of the decision making power to their subordi-
nates. Yet, in a study comparing Russian managers with us managers it
was found that middle level managers in Russia enjoy less authority in
decision-making than their us counterparts (Puffer and McCrathy 1993).
Today's Russian managers believe that with good management they can
achieve most of their organization's objectives and therefore they are
geared towards doing business. They have adopted the us belief that
time is a scarce resource and therefore they struggle to achieve as much
as possible within time constraints. While greasing palms to promote
business they still demonstrate personal trust, even though it sometimes
may mean over-promising and cutting corners (Puffer 1994). By doing
so, new entrepreneurs tarnish the reputation of private enterprises.
Critical abilities that may lead to the success of Russian managers are
networking, socializing, and politicking, followed by motivating and re-
warding subordinates (Luthans et al. 1993). Puffer and Shekshnia (1994)
found that the more foreign and the more privatized the company is the
better will be workers' compensation.
Theories of leadership (Hersey and Blanchard 1993; House 1974) sug-
gest that the more mature the employees are in their jobs and the more
familiar they are with their specific tasks, the more participative would
be the leader's style. A positive profile of leadership should reduce the
amount of uncertainty inherent in workers' tasks and, therefore, en-
hance workers' sense of control with regard to receiving their rewards.
Employees in state-owned enterprises have been employed by their or-
ganizations longer than employees in the private sector because the pri-
vate sector is a new creation in Russia. It is assumed that since employees
in state-owned organizations have been there longer than their counter-
parts in the private sector they are more familiar with their jobs. It is also
assumed that since employees in state-owned organizations are better
prepared to carry out their jobs they enjoy a more participative leader-
ship style than their counterparts in the private sector, and therefore they
would perceive their leaders to be more positive than would employees
in private enterprises perceive their managers to be. Consequently, the
following hypothesis is delineated:
Hypothesis 1: Russian workers employed in state-owned enterprises
perceive their organizations' leadership more positively than their
counterparts in private companies.
job characteristics
One job diagnosis that can be used to delineate variance in organiza-
tional leadership appears to be the Hackman and Oldham's (1975,1980)
Job Characteristics Model. Banai and Teng (1996) found that Russian
workers employed in state-owned enterprises enjoyed more enriched
jobs than their counterparts employed in the private sector. We suggest a
set of hypotheses relating to seven job characteristics, namely: autonomy,
feedback from agents, feedback from job, dealing with others, task iden-
tity, task significance and skills variety that correspond with Hackman
and Oldham's structure. These are hypothesized to differentiate between
state-owned and private companies as follows:
Autonomy. Russian private companies are by definition small newly es-
tablished companies that are managed by one or a few partners/owners.
Owner managers were not trained in Western-style management. They
are concerned with relinquishing too much knowledge to their subordi-
nates fearing that the subordinates would 'steal' their contacts and there-
fore their companies. They are also afraid that employees would leak in-
formation to the competition to be used against their own companies.
This attitude is not unique to Russian private businesses and could also
be found in Western organizations. Yet, the fear of 'stealing the business'
is not common in state-owned monopolies. Hence, managers in private
businesses would refrain from delegation of authority to their employees.
With less delegation from their managers, workers in private companies
would possess very little autonomy.
Hypothesis 2: Russian workers employed in state-owned enterprises
are perceived to enjoy more autonomy than their peers employed
in private companies.
Feedback from job and from agents. As earlier suggested managers in
private companies limit workers' contacts with suppliers, customers and
others, fearing that the workers will take advantage of the networking
to create a competition for the newly established business. Hence, work-
ers in private companies would generally have less feedback than their
counterparts in state-owned companies.
Hypothesis 3: Russian workers employed in state-owned enterprises
are perceived to enjoy more feedback from job than their peers em-
ployed in private companies.
Hypothesis 4: Russian workers employed in state-owned enterprises
are perceived to enjoy more feedback from agents than their peers
employed in private companies.
Dealing with others. Based on the same explanations delineated above,
workers in state-owned enterprises would have more contacts with enti-
ties external to the firms than workers in private companies.
Hypothesis 5: Russian workers in state-owned enterprises are per-
ceived to deal with others more than their peers employed in private
companies.
Task Identity. The newly established private company structures are
more likely to be simple production lines. Workers in small newly es-
tablished production lines have fewer opportunities to observe the final
product than workers in large state-owned enterprises in industries such
as grains (bakeries) and heating (installing and repairing electric and gas
appliances) or other state monopolies.
Hypothesis 6: Russian workers employed in state-owned enterprises
are perceived to enjoy more task identity than their peers employed
in private companies.
Task Significance. Extending the logic applied regarding the first five
hypotheses and based on the description that workers in privately owned
companies experience less enriched jobs than employees in state-owned
enterprises, the following hypothesis is formulated:
Hypothesis 7: Russian workers employed in state-owned enterprises
are perceived to enjoy more task significance than their peers em-
ployed in private companies.
Skills Variety. Extending the logic applied regarding the first five hy-
potheses and based on the description that workers in privately owned
companies experience less enriched jobs than their counterparts in state-
owned enterprises, the following hypothesis is formulated:
Hypothesis 8: Russian workers employed in state-owned enterprises
are perceived to enjoy more skills variety than their peers employed
in private companies.
Methods
setting
The study was conducted in Kazan, the capital city of Tatarstan, Rus-
sia. The sampling has been conducted at two points in time. In the first
sample three private companies and two state-owned companies were
studied. Among private companies there were a specialty shoes factory, a
wholesale trading firm, and a plastic consumer products manufacturer.
Local entrepreneurs created these three companies from scratch. They
had to secure facilities, machinery, raw materials, labour, and financing.
The state-owned companies included a major polymer production fac-
tory and a utility company. The factory was the largest in the city and the
utility company had a monopoly in providing energy to the city.
A private plastic consumer product plant and four state-owned com-
panies were sampled in the second case. The state-owned companies
included a utility company, a grain products company, an oil products
company, and a gelatin company, all major employers in the city.
The sample employed in this study was quasi-random. State-owned
and private firms were used as proxy indicators of what might be found if
one can get a representative sample of firms in Russia. State-owned com-
panies were large enough to allow for a random sampling of employees
by the administration. Managers were instructed by the researchers to go
through the list of workers in production and manufacturing functions
in their organization and, based on the size of the company, to ask ev-
ery (n) person to complete a questionnaire. To control the type and level
of job, managers and service people were omitted from the study. Due to
the small size of the private specialty shoes factory and the private trading
company, all employees were asked to be included. The plastic products
company was large enough for a random sampling of the employees.
The final statistics of the responses in the first sample are as follows:
In the private plastic company, where about 150 people were employed,
50 employees received questionnaires and 36 completed them (72% re-
sponse rate). In the private trading and shoe company, 18 out of a total
of 25 employees responded to the survey (72%). The state-owned utility
company had about 3000 employees of whom 100 were approached and
93 (92%) completed the questionnaire. In the state-owned polymer com-
pany with around 3,000 employees, 100 were approached and 61 (61%)
completed the questionnaire.
The statistics for the second sample are as follows: A private plastic
company with 300 employees was sampled and 86 out of 100 (86%) com-
pleted the questionnaire. In the state-owned utility company with 3000
employees 95 out of 100 (95%) completed the questionnaire, while in the
gelatin company where about 300 employees were employed 27 out of 50
(74%) completed the questionnaire. 225 out of 300 (75%) completed the
questionnaire in the grains company where about 1000 employees were
employed, and 66 out of 100 (66%) answered the questionnaire in the oil
company where about 300 people were employed. In total, 724 out of 950
(76%) respondents completed the questionnaire.
As can be learned from the statistics, state-owned companies were
much larger than private ones. The newly established private companies
were at the beginning of their life cycle, and therefore they were small.
The state-owned companies have been there for many years and they
were large.
sample
Education. All but a few of the 724 employees received at least a high
school diploma. The scale of this measure, ranging from 1 to 5, rep-
resents the following degrees: high school, associate, bachelor, master,
and Ph.D. respectively. Employees in state-owned companies had signif-
icantly (p = .00) more education (m = 2.35, sd = .85) than their counter-
parts in private companies (m = 2.02, sd = .80).
Age. The average respondent was 36.6 years of age (n = 724); employees
in private companies were 34.0 years of age (n = 158) while the age of
employees in state-owned companies was 37.5 years (n = 566).
Tenure. Since all companies in the private sector have been recently
founded, the average job tenure of private sector employees was only 3.65
years (n = 158) compared with 11.5 years for employees in state-owned
companies (n = 566).
Gender. Ten percent of all respondents were women. There was no sig-
nificant difference between the presence of women in state-owned and
private companies.
Too many missing cases limited our ability to introduce the bio-
demographic variables into bi-variate and multivariate analyses. The
incompletion of the bio data information is a result of the fact that while
questions regarding all other items were photocopied on one side of the
page of the questionnaire some of the questions regarding bio informa-
tion were written on the backside of the page causing many respondents
to miss it.
PROCEDURE
The survey data for this study were collected through questionnaires
and interviews. A graduate student from Russia, under the supervision
of a management professor who is bilingual, translated the question-
naire from English into Russian. A second graduate student from Rus-
sia translated the questionnaire back from Russian to English. Any re-
sulting discrepancies between the two versions were then resolved. This
back-translation technique has been advocated in cross-national studies
in order to provide reliability to the questionnaire (Brislin 1980; Rosen-
thal and Rosnow 1991). One of the authors controlled the distribution of
questionnaires to employees by their managers. The workers were gath-
ered for the distribution and they completed the questionnaires without
disclosing their names. Confidentiality was assured. Once completed, the
questionnaires were then turned to the author and therefore there was no
interference of management in the process.
MEASURES AND STATISTICAL ANALYSES
All participants were asked to complete a questionnaire that contained
measures of leadership, job characteristics, and background information
such as age, gender, education, and work experience.
Leadership. The instrument measuring leadership used (Korman 1994)
is based on the expectancy theory (Vroom 1964), which suggests that
table 1 Loadings of 3 perceived leadership-style factors
Item	F1	F2	F3
Organization makes jobs as interesting as possible	.81	.08	.05
Organization emphasizes performance evaluation and employees growth	.78	.03	.13
Organization's rewards system is clear and consistent	.76	.01	.22
Organization leadership all powerful	.75	.12	.14
Organization states the problem it is facing in realistic and straight forward terms	.75	.13	.06
Organization rewards good job performance	.74	.19	.16
Org. structured for independent decision making	.72	-.08	-.02
Organization states its plans realistically	.71	.30	-.03
There are long range organizational goals	.68	.22	.11
Organization uses mistakes for learning	.64	.25	-.15
Job provides opportunity for individual initiative	.57	.14	-.13
Employees know and understand the standards for effective job performance	.16	.76	.11
Job rules and/or performance guidelines exist	.24	.75	-.12
Organization requires to perform unethically	.09	.43	.32
Employees have a sense of control in the organisation	.10	-.12	-.68
Organization is hesitant in stating long term goals	.06	.06	.68
Organization takes negative view of the world	•33	-.27	.52
Eigenvalue	6.36	1.44	1.38
Percentage of variance	37.4	8.5	8.1
Cumulative percentage	37-4	45-9	54.0
Notes: F1 - positive leadership; F2 - performance management; F3 - negative leadership.
N = 724.
the level of work motivation is a function of valence, instrumentality,
and expectancy. The instrument used here focuses on these aspects of
leadership style and it contains 17 items. The Cronbach Alpha reliability
test of the internal consistency of the items is .87.
A factor analysis procedure measuring the perceived leadership yielded
three factors (see table 1).
The first factor contains 11 items and it has been labelled 'positive lead-
ership'. The Cronbach Alpha reliability coefficient value for the factor is
.92. The second factor, containing three items, reflects performance man-
agement and has been therefore labelled 'performance management'. The
Cronbach Alpha reliability coefficient value for the factor is .52. The third
factor is limited to three negative aspects of leadership and has been la-
belled 'negative leadership'. The Cronbach Alpha Reliability coefficient
value for this construct is .54. Though the last two values are below the
level recommended by the literature we used the newly established con-
structs because of the exploratory nature of this study.
Job Characteristics. The Job Characteristics Model (Hackman and Old-
ham 1974) differentiates organizations by the prevalence of seven charac-
teristics: autonomy, task identity, task significance, skill variety, feedback
from job, feedback from agents, and dealing with others. According to
Hackman and Oldham (1980) these characteristics are positively related
to a number of desirable organizational outcomes, such as higher inter-
nal work motivation and job satisfaction. The model was criticized for
failing to distinguish between the objective characteristics of jobs and the
respondents' perception of job characteristics (Roberts and Glick 1981).
However, the validity of the model was generally supported by empirical
studies (Fried and Ferris 1987) in the usa as well as in other countries
(Birnbaum, Farh and Wong 1986).
The Cronbach Alpha reliability test of the internal consistency of 21
items is .78.
In this study a factor analysis procedure revealed six factors with eigen-
value greater than one (see table 2).
The factors yielded in this analysis correspond reasonably with the-
oretical constructs proposed by Hackman and Oldham (1974). Out of
seven original factors proposed in Hackman and Oldham's model we
were able to replicate six factors. Factor 1 (jc1) includes a variety of items,
two of which are concerned with autonomy; hence, it has been labelled
'autonomy'. The Cronbach Alpha reliability coefficient value for the fac-
tor is .84. Factor 2 (jc2) is comprised of'feedback from agent' items (Al-
pha = .83); two items out of four on the third factor (jc3) belong to the
'dealing with others' construct (Alpha = .74), and two out of three items
on the fourth factor (jc4) belong to the 'task identity' construct (Al-
pha = .67). The fifth factor (jc5) includes three items that belong to three
different theoretical constructs. However, all three could be interpreted
to indicate 'task significance' (Alpha = .54). The last factor (jc6) includes
two items, one of which is feedback from job, and is therefore entitled
'feedback from job' (Alpha = .53). Oldham and Hackman's original con-
struct of skills variety did not show up in the factor analysis procedure.
table 2 Loadings of 6 job characteristics factors
Items	Original	F1	F2	F3	f4	F5	F6
To decide on your own how to work	AUT	•77	.04	.10	.08	-.04	.02
To see the end result	TKI	•73	.13	.09	.01	-.09	-.14
Performance info. is provided by job	fbj	•58	.22	.18	.21	.07	.00
To do many different things at work	skv	•51	.07	.39	-.07	.30	.09
The job affects the life of people	tks	•49	.32	.20	.03	.20	.19
Freedom in how to do your job	AUT	•47	.17	-.06	.18	.25	.37
Feedback about your performance	fba	.15	. 84	.15	.07	.04	-.01
Supervisor's evaluation of performance	fba	.09	.84	.15	.07	-.04	-.01
To know how well you are doing	fba	.28	.80	.11	-.00	.01	-.00
A lot of cooperative work	dwo	.07	.09	.80	.11	.12	.03
To work closely with other people	dwo	.20	.17	. 74	-.09	-.21	-.03
People are affected by your job	TKS	.13	.29	.59	.15	.23	.10
To use a number of high level skills	skv	.26	.21	.44	.02	.38	.13
To do an entire piece of work	TKI	.09	.08	.00	.79	-.08	.02
To complete the job to end	TKI	.06	-.03	.01	.77	-.13	.11
The job provides feedback	fbj	.18	.18	.37	•52	.12	.02
The job is unimportant to other people	TKS	-.08	.08	.02	.21	•67	-.10
The job is simple and repetitive	skv	.13	.02	.01	-.24	•63	-.18
The job can be done by one person	dwo	.05	-.15	.13	-.26	•61	-.02
Personal initiative in carrying out the job	AUT	.02	.07	.03	-.02	-.10	.77
Clues about performance from job	fbj	-.02	-.07	.08	.12	-.12	•74
Eigenvalue		4.86	2.13	1.49	1.29	1.28	1.12
Percentage of variance explained		23.1	10.1	7.1	6.1	6.1	5.4
Cumulative percentage		23.1	33.2	40.4	46.5	52.7	58.0
Notes: F1 - autonomy (aut); F2 - feedback from agents (fba); F3 - dealing with others
(dwo); F4 - task identity (ti); F5 - task significance (ts); f6 - feedback from job (fbj).
N = 724.
Significant statistical differences between the means of workers' atti-
tudes in state-owned companies and private companies were calculated
using anova tests. Finally, logistic regression was conducted to reveal
the variables that contribute to the explanations of differences in per-
ceived leadership style and job characteristics in state-owned and private
enterprises. The findings are presented below.
table 3 Pearson correlations among job characteristics and leadership style
Variables	jc1	jC2	jc3	jc4	jc5	jc6
LD1	.23**	.52**	.12**	-.04	.12**	-.04
LD2	.01	.20**	.37**	.18**	.01	.10*
ld3	.09*	-.07	.02	.05	.09*	-.28**
Notes: jc1 - autonomy; jc2 - feedback from agents; jc3 - dealing with others; jc4 - task
identity; jc5 - negative job characteristics; jc6 - feedback from job; ld1 - positive lead-
ership; ld2 - performance management; ld3 - negative leadership; * p < .05; ** p < .01.
N = 724.
Findings
Correlations between job characteristics and leadership are presented in
table 3.
Positive leadership is positively and significantly correlated with au-
tonomy, feedback from agents, dealing with others, and task signifi-
cance. Negative leadership is negatively and significantly correlated with
feedback from job, and positively with autonomy, and task significance.
Performance management is positively and significantly correlated with
feedback from agents, dealing with others, task identity, and feedback
from job.
Table 4 presents anova for 3 factors obtained for the Leadership Style
Model and 6 factors obtained for the Job Characteristics Model in the
private and state-owned organizations.
leadership style by ownership
Performance management was significantly lower in private organiza-
tions (m = -.02; sd = .75) than in state-owned enterprises (m = -.06;
sd = 1.05). Negative leadership was significantly lower in state-owned
enterprises (m = .05; sd = 1.00) than in private organizations (m = .18;
sd = .75). Hence, two out of three factors measuring different constructs
of leadership in this study were found to follow the hypothesis. Posi-
tive leadership was not found to cause a distinction between private and
state-owned enterprises.
job characteristics by ownership
Three out of 6 hypotheses tested for the relationship between job char-
acteristics and ownership type (private versus state-owned) were con-
firmed. Feedback from agents was significantly lower in private orga-
nizations (m =-.15; sd = .98) than in state-owned enterprises (m =-.04;
Corporate Ownership, Leadership and Job Charateristics 129
table 4 Leadership style and job characteristics: Comparison between private
companies and state-owned enterprises
Leadership style	Private*		State-owned**			
	Mean	sd	Mean	sd	f	Sig.
ld1 positive leadership	.05	1.02	-.01	1.00	.44	.51
ld2 performance management	-.02	.75	.06	1.05	8.02	.00
ld3 negative leadership	.18	•75	.05	1.00	6.03	.01
Job characteristics
jc1 autonomy
jc2 feedback from agents
jc3 dealing with others
jc4 task identity
jc5 negative characteristics
jc6 feedback from job
Notes: * n = 158 ; ** n = 566; N = 724.
sd = 1.00). Dealing with others was significantly lower in private orga-
nizations (m =-.32; sd = .85) than in state-owned enterprises (m =-.09;
sd = 1.02). Feedback from job was significantly lower in private organi-
zations (m = -.14; sd = 1.00) than in state-owned enterprises (m = .04;
sd = 1.00), thus hypotheses 3, 4, and 7 are corroborated. Hypotheses 2,
5, and 6, suggesting significant differences by sector, were not found to
be significant, and were therefore not confirmed. Hypothesis 8 could not
be tested.
multivariate logistic regression
In order to learn about the multivariate profile of being employed either
in private or in state-owned enterprises a multivariate logistic regression
analysis was performed employing 3 factors representing leadership style,
6 factors representing job characteristics, and the variable of the sample
which differentiated between sample one and sample two.
The results presented in table 5 demonstrate that two out of three lead-
ership style factors show a significant difference by type of ownership.
Additionally, state-owned enterprises are characterized by 3 out of 6 job
characteristics that are significantly different from job characteristics in
private enterprises. Specific results are described here.
Performance management is positively and significantly correlated
-.06	1.00
-.15	.98
-.32	.85
.02	.77
-.09	1.04
-.14	1.00
.02	1.00
-.04	1.00
-.09	1.02
-.00	1.06
.028	.99
.04	1.00
.62	.43
4.23	.04
19.99	.00
.11	.74
1.64	.20
3.38	.05
table 5 Logistic regression of private and state-owned enterprises on job characteristics and leadership style factors				
Variable	b	se	Wald	Sig
Constant	.57	.17	11.51	.00
Sample/Year	1.10	.22	24.15	.00
ld2 job evaluation	.29	.12	6.08	.01
ld3 negative leadership	-.31	.11	8.62	.00
jc2 feedback from agents	-.38	.11	11.57	.00
jc3 dealing with others	.43	.11	14.87	.00
jc5 task significance	.21	.10	4.14	.04
Variables excluded from the equation: jci - autonomy; jc4 - task identity; jc6 - feed-
back from job; ldi - positive leadership. N = 724.
with employment in state-owned enterprises (b = .29; se = .12; wald
test of significance = 6.08; Sig. = .01)
Negative leadership is positively and significantly correlated with em-
ployment in private organizations (b =-.31; se = .11; wald test of signif-
icance = 8.62; Sig. = .01).
Feedback from agents is positively and significantly correlated with
employment in private organizations (b = -.38; se = .11; wald test of sig-
nificance = 11.57; Sig. = .00).
Dealing with others is positively and significantly correlated with em-
ployment in state-owned enterprises (b = .43, se = .11; wald test of sig-
nificance = 14.87; Sig. = .00).
Task significance is positively and significantly correlated with em-
ployment in state-owned enterprises (b = .21; se = .10; wald test of sig-
nificance = 4.14; Sig. = .04)
The model is significant (chi-square = 62.61, df= 6; Sig. = .00, -2 Log
Likelihood = 583.95 and Goodness of Fit = 552.43). The overall prediction
power is 75.25%.
Discussion and Conclusions
As Russia is moving from a centrally planned economy to a free mar-
ket economy, its workers in the private and state-owned enterprises are
changing the perceptions of their organizational leadership and jobs. A
profile constructed of two elements of leadership style and three ele-
ments of job characteristics may best predict that a change occurred in
the perceptions of workers in private enterprises and not in state-owned
companies. State-owned organizations are characterized by a leader-
ship style that maximizes 'performance management' and minimizes
'negative leadership,' and job characteristics that include 'feedback from
agents,' 'dealing with others' and 'task significance.'
This profile could be regarded as part of the culture of the enterprises
studied. Perceived negative organizational culture may spill over into
employees' perceptions of their leadership and, among others, of their
jobs. This major finding could be used by researchers and managers of
transitional organizations. Researchers should aim to use holistic meth-
ods in analyzing transitional organizations, methods that measure and
control as many organizational variables as possible. Managers should
aim to improve not only their leadership style but also job characteristics
of their employees as part of their improvement of the culture of their
organizations.
While a profile of characteristics enables scholars and managers to
look at the big picture, the study results could be used also to discuss
each factor's potency in predicting the ownership system.
The first hypothesis suggested that Russian workers employed in state-
owned enterprises perceive their organizations' leadership more posi-
tively than their counterparts in private companies. This hypothesis has
been corroborated by two independent explanatory factors that have in-
dicated differences in the perceptions of workers in private and state-
owned enterprises. The first independent explanatory factor is perfor-
mance management. This factor includes items on a leadership question-
naire that refer to workers' jobs, such as understanding the standards
for an effective job performance, existing job rules and/ or performance
guidelines, and requirements by the organization to perform unethically.
While in both private and state-owned enterprises there was a negative
shift in this factor over the years, the changes in state-owned enterprises
were much more significant. It seems that workers in state-owned enter-
prises have changed the perceptions of their leadership to be less and less
definitive and clear about the leadership expectations from the workers.
The second independent explanatory factor that may explain differ-
ences between workers' perceptions of their leaders in private and state-
owned enterprises is the factor of negative leadership. It includes items
such as employees who have a sense of control in the organization, man-
agement that is hesitant in stating long-term goals, and leadership that
takes a negative view of the world. This factor that was perceived to pre-
vail in state-owned enterprises has improved in privately owned enter-
prises. Employees in private companies see their organizations' leader-
ship to be more negative and less orderly than their counterparts in state-
owned enterprises.
The second hypothesis suggested that Russian workers employed in
state-owned enterprises are perceived to enjoy more autonomy than their
peers employed in private companies. This hypothesis has not been cor-
roborated by the data and therefore it is rejected. Autonomy on the job
could be an imperative of the nature of the job itself, which is mostly a
result of the technology employed (Woodward 1958), rather than a con-
sequence of the ownership type.
The third hypothesis suggested that Russian workers employed in
state-owned enterprises are perceived to enjoy more feedback from job
than their peers employed in private companies. Feedback from job
could not explain differences between jobs in state-owned and private or-
ganizations and therefore the third hypothesis has been rejected. Again,
feedback from job may be a function of the technology used rather than
the ownership's type of the organization.
The fourth hypothesis has predicted that Russian workers employed in
state-owned enterprises are perceived to enjoy more feedback from agents
than their peers employed in private companies. This independent fac-
tor explained differences between jobs in state-owned and private en-
terprises and therefore hypothesis four has been corroborated. Feedback
from agents - includes feedback on job performance (the worker knows
how well he or she is doing on his/her job), and supervisor's evaluation
of job performance. While over the year workers in private enterprises
have learned to ask and to receive this feedback, workers in state-owned
enterprises have lost some of the same feedback. The first possible expla-
nation is that in the private enterprises performance is strongly linked to
rewards and therefore it is not surprising that, as the economy is shifting
to a more competitive mode, managers and workers have focused on this
issue. On the contrary, workers in state-owned enterprises, which tradi-
tionally have not linked performance to rewards, just got confused over
this issue in the face of the changing societal values.
The second possible explanation is that facing weakening state-owned
enterprises, managers actually could not provide workers with a good
performance management since the managers themselves did not have
the authority or the means to link employees' performance to their re-
wards.
The third possible explanation is that this finding may be a statement
of self justification, as if workers in state-owned enterprises were trying
to tell that job security in their companies is independent of the em-
ployees' performance and is therefore preferable to a job in the private
enterprises where non performance might lead to dismissal.
The fifth hypothesis suggested that Russian workers in state-owned
enterprises are perceived to deal with others more than their peers em-
ployed in private companies. This hypothesis has been corroborated, as
dealing with others has been found to be a significant explanatory factor
of the differences between jobs in private and state-owned enterprises.
Dealing with others includes items such as: cooperative work, working
closely with other people, affecting other people by one's job, and the
use of high level skills on the job.
As workers in private enterprises perceived their jobs to include more
and more elements referring to dealing with others, workers in state-
owned enterprises seem to have lost some of these aspects over the years.
The first possible explanation is that in private companies workers have
been forced to work closely with each other to solve operational prob-
lems while workers in state-owned enterprises continue to refer to their
supervisors to make decisions for them, as used to be the tradition in the
central planning economy, and therefore they do not have to cooperate in
their work activities. A second possible explanation is that as private or-
ganizations are improving their performance, workers have to take more
and more responsibility, and hence they have to cooperate in their jobs.
In state-owned enterprises, there are no incentives for taking responsibil-
ity and therefore workers do not really care about the end results of their
jobs and, hence, would not bother to cooperate to solve organizational
or operational problems.
The sixth hypothesis suggested that Russian workers employed in
state-owned enterprises are perceived to enjoy more task identity than
their peers employed in private enterprises. This hypothesis has been
rejected. Task identity is the extent to which the worker can see the end
results of his/her job and it may be related to the nature of the final
product/ service rather than to the organization's ownership type.
The seventh hypothesis suggested that Russian workers employed in
state-owned enterprises are perceived to enjoy more task significance than
their peers employed in private enterprises. Task significance encom-
passes items such as: a job unimportant to other people, a simple and
repetitive job, and a job that could be done by one person. While in pri-
vate organizations the tendency to perceive a job as less significant has
been reduced over the years, in state-owned enterprises it has increased.
Since it is plausible to believe that workers in state-owned enterprises
have changed very little of their job characteristics over one year, the
change in their perceptions could be attributed more to the change in
their attitudes towards their jobs rather than to a real job change. It is
possible that as a result of the changing societal perceptions of organiza-
tional efficiency and the private market shift in focus to high level skills
jobs, workers in private organizations see their jobs to be more positive
in general than workers in state-owned enterprises.
The eighth hypothesis suggested that Russian workers employed in
state-owned enterprises are perceived to enjoy more skills variety than
their peers employed in private enterprises. The hypothesis could not
be tested since the factor analysis procedure of the Job Characteristics
Model has not yielded a skills variety factor.
The study is not free of limitations. First, the survey results show that
there are differences in some aspects of leadership and job characteristics
between state-owned and private enterprises in Russia. The results do
not indicate which factors cause the differences. Ownership may be one
affecting factor, but not necessarily the definite one. Since the study did
not control the samples as a comparable set, there are many other plau-
sible factors contributing to the differences, such as a company's size or
age. Second, the sample that is comprehensive and includes many orga-
nizations is still quasi-random. Third, the sampling has been conducted
in one city in Russia and may not be representative of other Russian
places, after all Russia is a huge country with eleven time zones. Fourth,
despite the careful translation of the questionnaire it is difficult to esti-
mate its face value. Russian workers were not experienced in taking ques-
tionnaires and they may have found the whole experience confusing and
even threatening. Despite all these limitations, the study is unique in its
investigation of organizational attitudes during a major economic and
political transition.
From current knowledge about the transition in Russia it may be pos-
sible to infer that significant differences among workers could be better
identified in organizations that vary in their size and age, represent vari-
ous industries, and are located in certain regions of Russia, rather than in
state-owned and private enterprises. Moreover, since Russian economic
and political systems are still in transition it is possible that employees'
attitudes that were measured a few years ago have changed again. It is
therefore recommended to view this study's results as a snapshot that
has the potential to explain current differences between state-owned and
private enterprises in Russia, yet the application should be carefully done
by testing those attitudes again. A replication of this study may refine
the theories used to enable them to explain the relationship between job
characteristics and leadership style.
Local and foreign managers in Russia may realize that modern Russian
state-owned and private enterprises do not resemble public and private
sectors in the West. State-owned enterprises seem to be more stable and
less diffused in their activities, probably as a consequence of seventy years
of tradition. Private companies have just been created and therefore they
seem to be more chaotic and less focused than state-owned enterprises.
Yet, despite this tendency, it is likely that as the free market matures and
managers learn how to prioritize, manage and measure profit, their or-
ganizations will more and more resemble western-style private organi-
zations. Managers should learn how to integrate western-style manage-
ment into the Russian business environment to design better jobs for
their employees and to exercise a desirable leadership style. This may en-
hance the culture and performance of Russian organizations.
References
Adler, N. J. 1983. Cross cultural management research: The ostrich and the
trend. Academy of Management Review 8:226-32.
Adler, N. J. 1991. International dimensions of organizational behavior. Bel-
mont, ca: Wadsworth.
Allison, G. T. 1979. Public and private management: Are they fundamen-
tally alike in all unimportant respects? Proceedings for the Public Man-
agement Research Conference, November 19-20, 27-38. opm Document
127-53-1. Washington, dc: Office of Personnel Management.
Banai, M., and B. C. Teng. 1996. Comparing job characteristics, leadership
style, and alienation in Russian public and private enterprises. Journal
of International Management 2 (3): 201-24.
Barbosa, N., and H. Louri. 2005. Corporate Performance: Does ownership
matter? A comparison of foreign and domestic-owned firms in Greece
and Portugal. Review of Industrial Organization 27 (1): 73-102.
Bass, B. M. 1965. Organizational psychology. Boston, ma: Allyn and Bacon.
Becker E. R., and S. J. Potter. 2002. Organizational rationality, perfor-
mance, and social responsibility: Results from the hospital industry.
Journal of Health Care Finance 9 (1): 23-48.
Birnbaum, P. H., J. L. Farh, and G. Y. Wong. 1986. The job characteristics
model in Hong Kong. Journal of Applied Psychology 71:598-605.
Blake, A., and S. Mouton. 1985. The managerial grid ii. Houston: Gulf.
Bozeman, B. 1987. All organizations are public. San Francisco: Josey Bass.
Brislin, R. W. 1980. Translation and content analysis of oral and written
materials. In Handbook of cross cultural psychology, eds. H. C. Triandis
and J. W. Berry, 398-444. Boston, ma: Allyn and Bacon.
Buchanan, B. 1974. Government managers, business executives, and orga-
nizational commitment. Public Administration Review 34 (4): 339-47.
Buchanan, B. 1975. Red-tape and the service ethic: Some unexpected dif-
ferences between public and private managers. Administration & Soci-
ety 6 (4): 423-44.
Chubb, J. E., and T. M. Moe. 1988. Politics, markets, and the organization
of schools. American Political Science Review 82:1065-87.
Coursey, D., and H. G. Rainey. 1990. Perceptions of personnel system con-
straints in public, private, and hybrid organizations. Review of Public
Personnel Administration 10 (2): 54-71.
Employee Ownership Foundation. 2005. Employee ownership - A 'good'
business decision. Http://ww3.komotv.com/Global/story
.asp?S=3788369.
Fiedler, F. E. 1967. A theory of leadership. New York: McGraw Hill.
Fiedler, F. E. 1996. Research on leadership selection and training: One view
of the future. Administrative Science Quarterly 41:241-50.
Fried, Y., and G. R. Ferris. 1987. The validity of the job characteristics
model: A review and meta-analysis. Personnel Psychology 40:287-322.
Goldman, M. 1997. The pitfalls of Russian privatization. Challenge, May-
June, 35-49.
Hackman, J. R., and G. R. Oldham. 1974. The job diagnostic survey: An
instrument for the diagnosis of jobs and the evaluation of job redesign
projects. Springfield, va : Department of Commerce.
Hackman, J. R., and G. R. Oldham. 1975. Development of the job diagnos-
tic survey. Journal of Applied Psychology 60:151-70.
Hackman, J. R., and G. R. Oldham. 1980. Work redesign. Reading, ma:
Addison Wesley.
Hersey, P., and K. H. Blanchard. 1993. Management of organizational be-
haviour: Utilizing human resources. Englewood Cliffs, nj: Prentice
Hall.
Hofstede, G. 1983. Motivation, leadership, and organization: Do American
theories apply abroad? Organization Dynamics 9:42-63.
Hofstede, G. 1980. Culture consequences: International differences in work
related values. London: Sage.
House, R. J., and T. R. Mitchell. 1974. Path goal theory of leadership. Jour-
nal of Contemporary Business 3:81-97.
Kocenda E., and J. Svejnar. 2003. Ownership and performance after large-
scale privatization. Working Paper 471a, William Davidson Institute,
University of Michigan.
Korman, A. 1994. A measure of organizational leadership style. Unpub-
lished manuscript.
Lawler, E. E. 1981. Pay and organization. Reading, ma: Addison Wesley.
Luthans, F., D. H. B. Welsh, and S. A. Rosenkrantz. 1993. What do Russian
managers really do? An observational study with comparison to us
managers. Journal of International Business Studies 24 (4): 741-61.
Perry, J. L., and L. W. Porter. 1982. Factors affecting the context for motiva-
tion in public organizations. Academy of Management Review 7:89-98.
Perry, J. L., and H. G. Rainey. 1988. The public-private distinction in orga-
nization theory. Academy of Management Review 13:182-201.
Pierce J. L., and L. Rodgers. 2004. The psychology of ownership and
worker-owner productivity. Group & Organization Management 29
(5): 588-613.
Puffer, S. M. 1994. A portrait of Russian business leaders. Academy of Man-
agement Executive 8 (1): 41-54.
Puffer, S. M., and D. J. McCarthy. 1993. Decision making authority of for-
mer Soviet and American managers. The International Executive 35 (6):
497-512.
Puffer, S. M., and S. V. Shekshnia. 1994. Compensating local employees in
post communist Russia. Compensation & Benefits Review, September-
October, 35-43.
Rainey, H. G. 1983. Public agencies and private firms: Incentive structures,
goals, and individuals roles. Administration and Society 15:207-42.
Rainey, H. G. 1979. Perceptions of incentives in business and government:
Implications for civil service reform. Public Administration Review
39:440-8.
Rainey, H. G., R. W. Backoff, and C. H. Levine. 1976. Comparing public
and private organizations. Public Administration Review, March-April,
233-44.
Roberts, K. H., and W. Glick. 1981. The job characteristics approach to task
design: A critical review. Journal of Applied Psychology 66:193-217.
Rosenthal, R., and R. Rosnow. 1991. Essentials of behavioural research:
Methods and data analysis. 2nd ed. New York: McGraw Hill.
Shama, A. 1995. From exploiting to investing: An empirical study of entry
strategies of us firms to the former Soviet bloc. Paper presented at the
Annual Meeting of the Academy of Management, Vancouver, Canada.
Spender, J. C. 1993. Transferring management techniques to Eastern Eu-
rope. The International Journal of Organizational Analysis 1:237-54.
Solomon, E. E. 1986. Private and public sector managers: An empirical
investigation of job characteristics and organizational climate. Journal
of Applied Psychology 71:247-59.
Vroom, V. 1964. Work and motivation. New York: Wiley.
Woodward, J. 1958. Management and technology. London: hmso.
Joint Dynamics of Prices and Trading Volume
on the Polish Stock Market
Henryk Gurgul
Pawei Majdosz
Roland Mestel
This paper concerns the relationship between stock returns and trad-
ing volume. We use daily stock data of the Polish companies included
in the wiG20 segment (the twenty most liquid companies quoted on
the primary market of the Warsaw Stock Exchange). The sample cov-
ers the period from January 1995 to April 2005. We find that there is
no empirical support for a relationship between stock return levels and
trading volume. On the other hand, our calculations provide evidence
for a significant contemporaneous interaction between return volatil-
ity and trading volume. Our investigations reveal empirical evidence
for the importance of volume data as an indicator of the flow of in-
formation into the market. These results are in line with suggestions
from the Mixture of Distribution Hypothesis. By means of the Granger
causality test, we establish causality from both stock returns and return
volatility to trading volume. Our results indicate that series on trading
activities have little additional explanatory power for subsequent price
changes over that already contained in the price series.
Key Words: abnormal stock returns, return volatility, abnormal
trading volume, gArcH-cum-volume, causal relations
jel Classification: c32, g14
1 Introduction
Most empirical research about stock markets focuses on stock price
movements over time. The stock price of a company reflects investors'
expectations about the future prospects of the firm. New information
Dr Henryk Gurgul is a Professor at the Department of Applied
Mathematics, University of Science and Technology, Poland.
Dr Pawel Majdosz is an Assistant at the Department of Quantitative
Methods, School of Economics and Computer Science, Poland.
Dr Roland Mestel is an Assistant at the Institute of Banking and
Finance, University of Graz, Austria.
The authors thank two anonymous referees for their valuable
comments and suggestions on a previous version of the paper.
causes investors to change their expectations and is the main reason for
stock price changes.
However, the release of new information does not necessarily induce
stock prices to move. One can imagine that investors may evaluate the
news heterogeneously (as either good or bad). Think of a company that
announces an increase in dividend payout. Investors may interpret this
as a positive signal about the future performance of the company and
raise their demand prices. On the other hand, investors interested in
capital gains might wish to sell the stock on the basis of this informa-
tion, rather than receive dividend payouts (e. g. due to tax reasons). On
average, despite its importance to individual investors, such information
does not noticeably affect prices. Another situation in which new infor-
mation might leave stock prices unaltered can arise if investors interpret
the news homogeneously but start with different prior expectations (e. g.
due to asymmetrically distributed information). One can conclude that
stock prices do not mirror the information content of news in all cases.
On the other hand, a necessary condition for price movement is posi-
tive trading volume. Trading volume can be treated as descriptive statis-
tics, but may also be considered as an important source of information
in the context of the future price and price volatility process. Prices and
trading volume build a market information aggregate out of each new
piece of information. Unlike stock price behaviour, which reflects the av-
erage change in investors' beliefs due to the arrival of new information,
trading volume reflects the sum of investors' reactions. Differences in
the price reactions of investors are usually lost by averaging of prices, but
they are preserved in trading volume. In this sense, the observation of
trading volume is an important supplement of stock price behaviour.
In 1989 Poland, and thereupon other Eastern European countries,
started the transition process from a centrally planned economy to a
market economy. There was no pre-existing economic theory of such
a process to rely on. The early 1990s were extremely difficult for these
countries. Stock quotations on the wse were launched on April 16,1991.
This was the day of the re-establishment of the wse as the exclusive
place of trading on the Polish stock market after a break of more than
50 years. Continuous trading started in 1996, but only the most liquid
stocks were included in this system. Hence, an interesting question arises
as to whether the initial difficulties of the Polish stock market have now
been overcome, and whether the same mechanisms on the Polish stock
market as in developed capital markets can be identified.
To answer this question, we concentrate on the role of trading volume
in the process that generates stock returns and return volatilities on the
Polish stock market. Unlike most other studies on this issue, we use in-
dividual stock data instead of index data. Our investigation covers not
only contemporaneous but also dynamic (causal) relationships because
we are mainly interested in whether trading volume can be regarded as a
prognosis of stock return levels and/or return volatilities. One important
difference distinguishing this study from contributions in the existing lit-
erature is methodological. We do not use simple return and volume data
but replace these two variables with abnormal stock returns and abnor-
mal trading volume. To obtain these variables, we first calculate normal
(expected) returns and trading volume and then compute abnormal re-
alizations as the difference between the actual ex-post observations and
those expected from the model. Note that such a variable can be regarded
as a measure of the unexpected part of a given realization.
Our computations show that, on average, there is almost no relation-
ship between abnormal stock returns and excess trading volume in ei-
ther direction. It follows that knowledge of trading volume cannot im-
prove short-run return forecasts and vice versa. On the other hand, our
data support the hypothesis of a positive contemporaneous as well as
causal relationship between return volatility and trading volume. We find
that these results are mostly independent of the direction of stock price
changes. Finally, our models show that return volatility in many cases
precedes trading volume.
The rest of the paper is organized as follows. Section 2 contains a
brief overview of the existing literature on the relationship between stock
prices and trading volume. Section 3 describes our data, reports prelimi-
nary results, and also gives a detailed description of the applied method-
ology to obtain abnormal return and excess volume outcomes. Section 4
is dedicated to the tests used to check the contemporaneous relationship
between stock returns, return volatility and trading volume. Section 5 ex-
tends our analysis to the examination of dynamic (causal) relationships.
Section 6 concludes and provides suggestions for further research.
2 Existing Literature
An early work dedicated to the role of trading volume in the price gen-
erating process is that by Clark (1973). He developed the well known
Mixture of Distribution Hypothesis (mdh). This hypothesis argue that
stock returns are generated by a mixture of distributions. Clark states
that stock returns and trading volume are related due to the common
dependence on a latent information flow variable. According to Clark,
the more information arrives on the market within a given time interval,
the more strongly stock prices tend to change. The author advises the use
of volume data as a proxy for the stochastic (information) process. From
the mdh assumption it follows that there are strong positive contem-
poraneous but no causal linkages between trading volume and return
volatility data. Under the assumptions of the mdh model, innovations
in the information process lead to momentum in stock return volatil-
ity. At the same time, return levels and volume data exhibit no common
patterns. The theoretical framework developed by Clark has been gener-
alized among others by Epps and Epps (1976), Tauchen and Pitts (1983),
Lamoureux and Lastrapes (1990), and Andersen (1996).
An important model explaining the arrival of information on a mar-
ket is the sequential information flow model introduced by Copeland
(1976). It implies that news is revealed to investors sequentially rather
than simultaneously. This causes a sequence of transitional price equilib-
rium which is accompanied by a persistently high trading volume. The
most important conclusion from this model is that there exist positive
contemporaneous and causal relationships between price volatility and
trading activities.
In a framework which assumes stochastic fluctuations of stock prices,
recent studies, e. g. by Blume et al. (1994) and Suominen (2001) state that
data concerning trading volume deliver unique information to market
participants; information which is not available from prices. Blume et al.
argues that informed traders transmit their private information to the
market through their trading activities. Uninformed traders can draw
conclusions about the reliability of informational signals from volume
data. Therefore, return volatility and trading volume show time persis-
tence even in a case where the arrival of information does not show it.
As do Blume et al., Suominen (2001) applies a market microstructure
model in which trading volume is used as a signal to the market by unin-
formed traders and can help to reduce information asymmetries. These
two studies argue that trading volume describes market behaviour and
influences market participants' decisions. Both authors suggest strong
relationships, not only contemporaneous but also causal, between vol-
ume and return volatility.
These theoretical contributions have been accompanied by a number
of empirical studies which deal with volume-price relationships on cap-
ital markets. The most important findings are those by Karpoff (1987),
Hiemstra and Jones (1994), Brailsford (1996) and Lee and Rui (2002). The
cited authors mainly use index data. Although these studies differ sig-
nificantly with respect to sample data and applied methodologies, they
convey empirical evidence of the existence of a positive volume-to-price
relationship.
The interdependencies between stock return volatility and trading vol-
ume have been the subject of investigation by Karpoff (1987), Bessem-
binder and Seguin (1993), Brock and LeBaron (1996), Avouyi-Dovi and
Jondeau (2000), and Lee and Rui (2002). All these studies give evidence
of a strong relationship (contemporaneous as well as dynamic) between
return volatility and trading volume. In contrast to these authors, Darrat
et al. (2003), using intraday data from djia stocks find evidence of signif-
icant lead and lag relations only. They do not report a contemporaneous
correlation between return volatility and trading volume.
Lamoureux and Lastrapes (1990) were the first to apply stochastic time
series models of conditional heteroscedasticity (GARcH-type) in the con-
text of price-volume investigations. They analyzed the contemporaneous
relationship between volatility and volume. They found that the per-
sistence of stock return variance vanishes when trading volume is in-
cluded in the conditional variance equation. Considering that trading
volume is a proxy for the flow of information into the market, this result
supports the mdh. A paper by Lamoureux and Lastrapes (1990) gives
general proof of the fact that trading volume and return volatility are
driven by the same factors. They do not, however, answer the question on
the identity of these factors. Lamoureux and Lastrapes (1994), Andersen
(1996), Brailsford (1996), and Omran and McKenzie (2000) expanded
this GARcH-cum-volume approach.
3 The Data and Preliminary Results
Our data set consists of daily stock price and trading volume series for
all companies listed in the wig20 on April 29, 2005. The wig20 reflects
the performance of the twenty most liquid Polish companies in terms
of free float market capitalization. Our time series are derived from the
database of parkiet. The investigation covers the period from January
1995 to April 2005. An appendix at the end of the paper contains a list
of all companies included in the sample as well as their period of quo-
tation. We use continuously compounded stock returns calculated from
daily stock prices at close, adjusted for dividend payouts and stock splits.
As a proxy for return volatility we employ the squared values of daily
stock returns. We repeated all computations using absolute instead of
squared stock returns and find that the use of this alternative measure
for stock return volatility delivers almost the same results. To measure
trading volume the daily number of shares traded is being used.
descriptive statistics
We start with some basic descriptive analysis of the time series of stock
returns and trading volume. As can be seen from panel a of table 1, the
average daily stock return over the period under study ranges from -
0.28% (Netia) to 0.12% (bre) with a median of-0.05%. Standard devi-
ation is the lowest for pkn (1.85%) and the highest for Netia (4.45%).
The commonly reported fact of fat-tailed and highly-peaked return
distributions is being supported by most of our series. The median of
stock return kurtosis is 6.88 and ranges from 34.98 (sfc) to 3.9 (pkn).
Return skewness is the highest for Netia (0.92) and the lowest for sfc
(-1.8) with a median of 0.19. By applying Jarque-Bera and chi-square
goodness-of-fit tests for normality, we additionally find strong support
for the hypothesis that our return series do not come from a normal dis-
tribution. Concerning autocorrelation properties, the Ljung-Box Q-test
statistics for the 15th order autocorrelation provide evidence of signifi-
cant low-order autocorrelation in about 50% of all cases.
Unlike stock returns, both return volatility and trading volume com-
monly display strong persistence in their time series. By means of Ljung-
Box Q (15)-statistics we find strong support for the hypothesis that trad-
ing volume exhibits serial autocorrelation. Consistent with the stylized
facts of volume series listed by Andersen (1996), our volume data exhibit
a high degree of non-normality, expressed by their considerable kurtosis
and their being skewed to the right (see panel c of table 1).
As a proxy for return volatility we use the squared values of daily stock
returns. These time series display the usual time dependency of stock
returns in the second order moment (volatility persistence) implying,
among other things, that returns cannot be assumed to be i. i. d. As for
trading volume, the null hypothesis of squared returns coming from a
normal distribution is strongly rejected (panel b in table 1).
ABNORMAL RETURNS AND ABNORMAL TRADING vOLUME
One point that is essential in distinguishing our study from other contri-
butions is that we focus on interactions between abnormal stock returns
table 1 Aggregated summary statistics for stock market data of wiG20 companies
	Mean ■ 103	Std. dev. ■ 103	Skewness	Kurtosis
Panel a: Daily stock returns				
Min	-2.81	18.50	-1.80	3.90
1st Quartile	0.10	24.85	-0.07	6.50
Median	0.52	26.33	0.19	6.88
3rd Quartile	0.73	32.00	0.27	8.78
Max	1.15	44-49	0.92	34-98
Panel b: Daily squared stock returns				
Min	0.34	0.59	3.89	22.83
1st Quartile	0.62	1.47	5.83	49.55
Median	0.69	1.93	7.18	77.15
3rd Quartile	1.03	2.69	8.53	120.20
Max	1.99	7-83	35-35	1 435.61
Panel c: Daily trading volume				
Min	9.80	16.42	1.66	7.82
1st Quartile	28.57	41.03	2.98	17.24
Median	70.10	73.98	4.11	29.22
3rd Quartile	231.53	314.82	7.40	119.33
Max	1337-54	1286.82	34-97	1359-58
and abnormal trading volume, instead of simple return and volume data.
Since we concentrate on individual companies, instead of index data, our
goal is to establish unique firm-specific relationships, i. e. we filter out
systematic price and volume effects. For each trading day t we compute
the abnormal return ARit for company i as the difference between the
actual ex-post return and the security's normal (expected) return. For-
mally we have
AR1>t = Ri,t - E [R1>tIVj]	(1)
where R,t stands for the actual return of firm i on day t and ERt |It-1]
stands for the predicted (normal) return conditional on the information
set It-1.
To model risk-adjusted expected returns ERt|It-1] we use the Market
Model approach, which relates a security's return to the return of the
market. The latter is approximated in our study by the log-returns of
the wig, which comprises the majority of firms listed on the primary
market of the Warsaw Stock Exchange. For each day the relevant model
parameters are estimated by means of an ols method. The estimation
window comprises 100 trading days prior to that date. Since our analysis
starts on January 2,1995, this implies that the first realisation of abnormal
stock returns for each company can be observed for the 101st trading day
in 1995.
Abnormal trading volume is computed in a similar way. To isolate
information-related trading activity, we follow Tkac (1999) who found
that market-wide trading is also an important component of the trad-
ing activity of individual firms, and that it should be taken into account
when modeling volume time series. However, the application of a 'Vol-
ume Market Model' proposed in Ajinkya and Jain (1989) generates many
statistical problems. We find that the resulting abnormal volume series
mostly depart from the underlying model assumptions. This leads to
biased inferences. Taking this into account, we follow, among others,
Beneish and Whaley (1996) by using firm-specific average volume data
as a benchmark for normal trading volume. As was the case with the
estimation window for the return parameters in the Market Model, the
estimation window for the mean firm-specific volume also covers 100
trading days.
testing for unit root
Testing for causal relationships between trading volume and stock price
data can be sensitive to non-stationarities. Therefore, we check whether
the time series of stock returns and trading volume can be assumed to
be stationary by using the augmented Dickey-Fuller (adf) test. This is
necessary to avoid model misspecifications and biased inferences. The
adf test is based on the regression:
where yt stands for stock return or trading volume on day t, ß, 7 and 6
are model parameters, and st represents a white noise variable. The unit
root test is carried out by testing the null hypothesis of a unit root in the
stochastic process generating yt(7 = 0) against the one-sided alternative
7 < 0.
We conduct adf tests for each company's time series of stock returns.
We find the parameter 7 to be negative and statistically significant at rea-
sonable levels in all cases. The same is true for the time series of trading
p
(2)
table 2 Cross-correlation coefficients between abnormal stock returns (AR),
abnormal return volatility (AR2) and abnormal trading volume (AV)
j = -2 j = -1 j = 0 j = 1 j = 2
Panel a: Corr(ARt,AVH)
Min	-0.02
1st Quartile	0.03
Median	0.04
3rd Quartile	0.07
Max	0.13
Panel b: Corr(AR2t, AVt-j)
Min	-0.10	-0.06	-0.07	-0.08	-0.13
1st Quartile	0.05	0.08	0.09	0.03	0.01
Median	0.08	0.15	0.17	0.07	0.04
3rd Quartile	0.11	0.20	0.20	0.10	0.07
Max	0.17	0.30	0.32	0.14	0.13
-0.01	0.04
0.08	0.09
0.10	0.12
0.13	0.13
0.18	0.16
-0.03	-0.02
-0.01	-0.01
0.00	0.00
0.03	0.02
0.05	0.04
volume. Hence we come to the conclusion that both time series of stock
returns and trading volume can be assumed to be invariant with respect
to time.
cross-correlation analysis
At the beginning of our investigation of interactions between abnor-
mal stock return and abnormal trading volume data we calculate simple
cross-correlation coefficients Corr for all companies:
rAi? AVI Cov[ARt,AVt]	. .
Corr\ARt,AVt1 =-,	(3)
SD[ARt] ■ SD[AVt]
where ARt (AVt) denotes abnormal stock return (abnormal trading vol-
ume) on day t, Cov stands for covariance and SD is standard deviation.
From panel a of table 2 we see that there is no direct contemporaneous
correlation between abnormal stock return levels and excess trading vol-
ume. The same results are obtained when one computes Corr between
AR and lagged (leading) data of AV.
On the other hand, panel b of table 2 shows a positive contempora-
neous correlation between abnormal trading volume and abnormal re-
turn volatility. From this observation it follows that, due to its impact on
return volatility, trading volume might indirectly contain information
about stock price behaviour.
We also find an asymmetry in the cross correlation between squared
AR and AV around zero. In all cases, Corr[AR2t, AVt-j ] is greater for j = -1
than for j = 1. This fact is in line with the widespread expectation that
trading volume is, at least partly, induced by heavy price fluctuations.
4 Contemporaneous Relationship
stock RETURNS AND TRADING vOLUME
In this section we test the contemporaneous relationship between ab-
normal stock returns and excess trading volume. We use a multivariate
simultaneous equation model proposed by Lee and Rui (2002), which is
defined by the two equations:
ARt = ao + aiAVt + atARt-i + eu;
AV t = ßo + ßiARt + ßt AV t-i + ß3AVt-t + e%t.	(4)
We assume st to be white noise. One has to take into account that
the jointly determined endogenous variables in each equation are not
independent of the disturbances. This is important in respect to the esti-
mation process. To take this possible dependence into account, we apply
Full-Information Maximum Likelihood (fiml) methodology. fiml gen-
erates asymptotically efficient estimators. An additional advantage is that
the cross-equation correlations of the error terms are taken into account
(see e. g. Davidson and MacKinnon 2003). The significance of all coeffi-
cients in models (4), (5) and (6) (see below), is proved by means of the
t-Student test (t-ratio coefficients).
The findings are in line with our expectations of almost no essential
contemporaneous relationship between abnormal stock returns and ex-
cess trading volume. Across the whole sample, the parameters a1 and ß1
in (4) turn out to be statistically significant in only 4 cases. Since the ma-
jority of our abnormal return series exhibit no serial correlation, we find
parameter at to be significant in only 6 cases.
Time dependence in the trading volume time series is supported by
the highly significant values found for parameters ßt (16 cases) and ß3
(11 cases). As one would expect, the sign of these coefficients is positive
in all but two cases, implying positive autocorrelation in volume data.
Even though we find abnormal stock return levels and trading volume
to be mutually independent, this does not mean that no relationships
can be found in these market data at all. Several authors report that price
fluctuations tend to increase in face of high trading volume. Therefore,
a relation might exist between higher order moments of excess stock re-
turns and trading volume.
In addition, we check whether this volatility-volume relationship is the
same irrespective of the direction of the price change, or whether trading
volume is predominantly accompanied by either a large rise or a large fall
in stock prices. We test this by using a bivariate regression model, given
by the following equation:
AV t = ao + (piAV t-i + (piAV— + aiAR2t + a2DtAR2t + et. (5)
In model (5), Dt denotes a dummy variable that equals 1 if the cor-
responding abnormal return ARt is negative, and 0 otherwise. The esti-
mator of parameter a1 measures the relation between abnormal return
volatility and excess trading volume, irrespective of the direction of the
price change. The estimator of a2, however, reflects the degree of asym-
metry in this relationship. To avoid the problem of serially correlated
residuals, we include lagged values of AV up to lag 2. After this, we find
the error term st in equation (5) to be largely serially uncorrelated.
By means of the ml method we estimate equation (5). According to
our computations, the estimate of parameter is significant in 17 cases
and the estimate of parameter 02 is significant in 15 cases. We also estab-
lish that parameter a1 is positive and significant for all but 2 companies.
This is in line with our earlier hypothesis of a strong contemporaneous
relationship between squared AR and AV. The estimate of parameter a2
is significant in 13 cases and negative in all of these. We find that for our
sample of the Warsaw Stock Exchange, strong price changes are always
accompanied by an increase in trading volume, irrespective of the direc-
tion of price fluctuations.
trading volume and volatility
The stochastic process of stock returns is given by means of an aug-
mented Market Model with an autoregressive term of order 1 in the
conditional mean equation below. The conditional variance is captured
by an adapted gjr-garch(1,1) model (Glosten et al. 1993). In this ver-
sion, trading volume is included as an additional predetermined regres-
sor. The gjr model captures the asymmetric (leverage) effect discovered
by Black (1976), which states that bad information, reflected in an un-
expected decrease in prices, causes volatility to increase more than good
news. Engle and Ng (1993) supplied a theoretical and empirical support
and stated that, among alternative models of time-varying volatility, the
gjr model is the best at efficiently capturing this effect.
The model is represented by the following two equations:
Rt = ao + aiRt-i + a2Rm,t + et, et ~ (0, o-2t);
a2t = ht = ßo + ßi ht-i + ßt et-i + ßa S--i«2-i + jVt.	(6)
Here et is assumed to be distributed as t-Student with v degrees of
freedom conditional on the set of information available at t -1; ot repre-
sents the conditional variance of et; and S-_ i is a dummy variable, which
takes the value of i in the case of the innovation et-i being positive and
0 otherwise. Model (6) rests upon the assumption that trading volume
is a proxy for the flow of information into the market: if return volatility
is in fact mostly influenced by the information flow, the effect of volatil-
ity clustering should decrease if one incorporates trading volume in the
conditional variance equation. In (6) the sum of parameters ßi and ß2
reflects the persistence in the variance of the unexpected return et, tak-
ing values between 0 and i. The closer this sum is to unity, the greater
the persistence of shocks to volatility (volatility clustering). The estimate
of parameter ß3 accounts for potential asymmetries in the relationship
between return innovation and volatility.
We apply a t-Student distribution for the return innovations et be-
cause we find this to fit our turnover ratio series best. Thus, we use the
conditional t-Student distribution for which the normal is a special case
(v > 30). For model (6), a likelihood function L is defined as:
'v + i\ , „/v)
■{^(IliJ-lnT^-IlnW^)]
it
t=i
ln(<7?) + (l + v)ln(l + -!-^
v - 2 ot
(7)
t
where T denotes the sample size and T(.) denotes the gamma function.
The model parameters are estimated by means of the ml method. As
a first step, we estimate the parameters of model (6) assuming that y is
equal to 0 (restricted variance equation, see table 3). We find that the
estimate of parameter ßi as well as the estimate of parameter ß2 is signif-
icant in nearly all cases. For 14 companies, the observed sum (ßi + ß2) lies
within the range [0. 9 - i]. The average is 0.93, which indicates high per-
sistence in conditional volatility. In most cases, ß3 is positive, but turns
out to be statistically significant for one company only. This indicates
that the asymmetric reaction of conditional variance to return innova-
tions is rather modest in our data. In the next step we are interested in
the unrestricted equation for conditional variance. We find parameter 7
table 3 Persistence in conditional stock return volatility [restricted versus
unrestricted version of model (6)]
Symbol	(ßl +ßlf	Cßl +ßi)h	Symbol	(ßl +ßl)a	(ßl +ß2t
AGO	0.93	0.08	KT Y	1.00	0.03
bph	0.98	0.97	NET	1.00	0.88
BRE	0.95	0.13	ORB	0.97	0.21
Bzw	0.81	0.90	PEO	0.87	0.91
cpl	0.99	0.08	PKM	0.96	0.06
cST	0.75	0.41	PKN	0.96	0.89
dbc	0.70	0.11	SFT	0.91	0.04
FSc	0.96	0.37	stx	0.96	0.89
KGH	0.98	0.89	TPS	0.98	O.83
			Average	O.93	0.48
to be positive and highly significant across the whole sample. Our data
show a considerable decrease in the persistence of volatility when trad-
ing volume is included in (6). The sum of parameters ß1 and ßt declines
for almost all companies. The mean falls from 0.93 to 0.48. The esti-
mate of parameter ßt shows a significant drop. In the unrestricted form
it becomes, for the most part, insignificant. Table 3 gives the degree of
persistence in variance, measured by the sum (ß1 + ßt) for the restricted
and unrestricted form of (6). Results are shown for all stocks under con-
sideration.
It cannot be derived from our data that trading volume is the true
source of persistence in volatility. Empirical results support the conjec-
ture that trading volume might itself be partly determined by return
volatility, causing a simultaneity bias in the coefficient estimates. To solve
this simultaneity problem we re-run model (6) substituting Vt-1 for Vt.
In line with Gallo and Pacini (2000), we find that volatility persistence
under this approach remains almost the same as in the restricted version
of (6). It can be concluded that contemporaneous trading volume is a
sufficient statistic for the history of return volatility. Despite this, our re-
sults can only partly be interpreted as an indication that the mdh holds
true.
5 Dynamic Relationship
Up to this point, our investigations focused exclusively on contempo-
raneous relationships between trading volume and stock returns, and
trading volume and return volatility. The following part of the paper
studies dynamic (causal) interactions between these variables. Testing
for causality is important because it permits a better understanding of
the dynamics of stock markets, and may also have implications for other
markets.
From section 3 we get a hint that it is probable that causality is present
in the relationship between return volatility and trading volume. This
hypothesis can be proved by means of the Granger causality test (Granger
1969). A variable Y is said not to Granger-cause a variable X if the dis-
tribution of X, conditional on past values of X alone, equals the distri-
bution of X, conditional on past realizations of both X and Y. If this
equality does not hold, Y is said to Granger-cause X. This is denoted by
Y — X. Granger causality does not mean that Y causes X in the more
common sense of the term, but only indicates that Y precedes X. In the
case of the feedback relationship (i. e. X Granger-causes Y and vice versa)
this relation is written as Y ^ X.
As a test of Granger causality, we apply a bivariate vector autoregres-
sion (var) of the form:
ARt = jU1 + J] auARt-t + ^ßuAV - + ,
£1,t;
i=1 i=1
pp
AV t = ß2 + 2 ®2,iAV t-i + ^ ßl,iARt-t + S2,t.	(8)
i=1 i=1
Model (8) is estimated using an ols method. In order to choose an
appropriate autoregressive lag length p of the var, we apply the Akaike
information criterion (aic). Based on this measure of goodness-of-fit,
we establish the proper lag length p to be equal to 2 for all companies.
In terms of the Granger causality concept, it is said that AR (AV) does
not Granger-cause AV (AR) if the coefficients ßi (i = 1,...,p) in (8),
respectively, are not significant, i. e. the null hypothesis H0: ß1 = ß2 =
. . . = ßp = 0 cannot be rejected.
To test the null, we calculate the F-statistic:
^ SSEo - SSE N - 2p - 1
F =---.	(9)
SSE	p
In (9) SSE0 denotes the sum of squared residuals of the regression
model constrained by ßi = 0 (i = 1,...,p), SSE is the sum of squared
residuals of the unrestricted equation, and N stands for the number of
observations. The statistic (9) is asymptotically F distributed under the
table 4 Number of rejected null hypotheses based on the Granger causality test
Panel a: Causality between excess trading volume and abnormal stock returns
AR AV AV ^ AR AR AV
Sample size: 18 companies	11	0	1
Panel b: Causality between excess trading volume and squared abnormal stock returns
AR2 AV AV ^ AR2 AR2 AV
Sample size: 18 companies	813
Level of significance is 5%. Order p in (8) is equal to 2.
non-causality assumption, with p degrees of freedom in the numerator
and (N - 2p - 1) degrees of freedom in the denominator.
Concentrating on the rejection of the null hypothesis of Granger non-
causality, panel a of table 4 demonstrates that abnormal returns (excess
trading volume) precede excess trading volume (abnormal returns) in
11 (0) cases. Both numbers reflect exclusively unidirectional causalities.
Only in one case a two-way causality (feedback relation) is detected. To
conclude, short-run forecasts of current or future stock returns in gen-
eral cannot be improved by the knowledge of recent trading volume data.
The observation that stock returns precede trading volume in approxi-
mately half of all cases is in line with similar findings by Glaser and Weber
(2004) and confirms predictions from overconfidence models. To sum-
marize, we find only weak evidence of causality between abnormal stock
returns and excess trading volume, especially causality running from
trading volume to stock returns. This is in line with our expectations.
To evaluate dynamic relationships between stock return volatility and
trading volume, we substitute the abnormal return level for the squared
values of abnormal stock returns, and re-estimate the model (8). Panel
b of table 4 confirms the existence of causal relationships from AR2 to
AV. In 10 cases, AR2 precedes AV, whereas in only 1 case does Granger
causality run from AV to AR2. This result is again in line with our earlier
finding that stock price changes in any direction have information con-
tent for upcoming trading activities. The preceding return volatility can
also be seen as some evidence that the arrival of new information might
follow a sequential rather than a simultaneous process.
Our results indicate that data on trading activity have only little addi-
tional explanatory power for subsequent price changes that is indepen-
dent of the price series. In this sense, our empirical results for the Polish
stock market does not overall corroborate theoretical suggestions made
by Blume et al. (1994) and more recently by Suominen (2001).
154 Henryk Gurgul, Pawel Majdosz, and Roland Mestel
6 Conclusions
Our paper presents a joint dynamics study of daily trading volume and
stock returns for Polish companies listed in the wiG20. We test whether
volume data provide only a description of trading activities or whether
they convey unique information that can be exploited for modeling stock
returns or return volatilities. These relationships are investigated by the
use of abnormal stock return and excess trading volume data. Our re-
sults give no evidence of a contemporaneous relationship between mar-
ket adjusted stock returns and mean adjusted trading volume. The lin-
ear Granger causality test of dynamic relationships between these data
does not indicate substantial causality. We can conclude that short-run
forecasts of current or future stock returns cannot be improved by the
knowledge of recent volume data and vice versa. This finding is in line
with the efficient capital market hypothesis. However, the Polish data
show extensive interactions between trading volume and stock price fluc-
appendix a Companies included in the sample, symbol legend
and period of quotation*
PLPKN0000018	20 April 1999 - 29 April 1999
PLPEKA000016	7 February 1995 - 29 April 2005
PLTLKPL00017	2 January 1995 - 29 April 2005
PLKGHM000017	2 January 1995 - 29 April 2005
PLBPH0000019	27 October 1995 - 29 April 2005
PLAG0RA00067	25 May 1998 - 29 April 2005
plbz00000044	2 January 1995 - 29 April 2005
PLPR0KM00013	22 April 1997 - 17 February 2005
PLNETiA00014	10 July 1997 - 29 April 2005
PLBRE0000012	30 January 1996 - 29 April 2005
PLSTLEx00019	11 July 2000 - 29 April 2005
PLKETY000011	20 November 1997 - 29 April 2005
PL0RBiS00014	30 June 1998 - 29 April 2005
PLS0FTB00016	10 February 1998 - 29 April 2005
PLcMPLD00016	26 November 1999 - 29 April 2005
PLcRSNT00011	2 June 1998 - 29 April 2005
plcelza00018	2 January 1995 - 29 April 2005
PLDEBcA00016	18 November 1998 - 29 April 2005
* In the case of two firms included in the wig20, data series have been too short.
tuations. We find that squared abnormal stock returns and excess trad-
ing volume are contemporaneously related. This implies that both time
series might be driven by the same underlying process. In contrast to
Brailsford (1996), our findings provide evidence that for the Polish stock
market this volatility-volume relationship is independent of the direc-
tion of the observed price change. We apply our investigations to a con-
ditional asymmetric volatility framework in which trading volume serves
as a proxy for the rate of information arrival on the market. The results
to some extent support suggestions of the Mixture of Distribution Hy-
pothesis, i.e. that arch is a manifestation of daily time dependence in
the rate of new information arrival. We also detect dynamic relationships
between return volatility and trading volume data.
References
Ajinkya, B. B., and P. C. Jain. 1989. The behavior of daily stock market
trading volume. Journal of Accounting and Economics 11:331-59.
Andersen, T. G. 1996. Return volatility and trading volume: An infor-
mation flow interpretation of stochastic volatility. Journal of Finance
51:169-204.
Avouyi-Dovi, S., and E. Jondeau. 2000. International transmission and
volume effects in G5 stock market returns and volatility. bis Confer-
ence Papers, no. 8:159-74.
Beneish, M. D., and R. E. Whaley. 1996. An anatomy of the s&p game.
Journal of Finance 51:1909-30.
Bessembinder, H., and P. J. Seguin. 1993. Price volatility, trading volume
and market depth: Evidence from futures markets. Journal of Financial
and Quantitative Analysis 28:21-39.
Black, F. 1976. Studies in stock price volatility changes. In Proceedings of the
1976 Business Meeting of the Business and Economics Statistics Section,
177-181. N. p.: American Statistical Association.
Blume, L., D. Easley, and M. O'Hara. 1994. Market statistics and technical
analysis: The role of volume. Journal of Finance 49:153-81.
Brailsford, T. J. 1996. The empirical relationship between trading volume,
returns and volatility. Accounting and Finance 35:89-111.
Brock, W. A., and B. D. LeBaron. 1996. A dynamic structural model for
stock return volatility and trading volume. The Review of Economics
and Statistics 78:94-110.
Clark, P. K. 1973. A subordinated stochastic process model with finite vari-
ance for speculative prices. Econometrica 41:135-55.
Copeland, T. 1976. A model of asset trading under the assumption of se-
quential information arrival. Journal of Finance 31:135-55.
Darrat, A. F., S. Rahman, and M. Zhong. 2003. Intraday trading volume
and return volatility of the djia stocks: A note. Journal of Banking and
Finance 27:2035-43.
Davidson, R., and J. G. MacKinnon. 2003. Econometric theory and meth-
ods. New York: Oxford University Press.
Engle, R. F., and V. K. Ng. 1993. Measuring and testing the impact of news
on volatility. Journal of Finance 48:1749-78.
Epps, T. W., and M. L. Epps. 1976. The stochastic dependence of security
price changes and transaction volumes: Implications for the mixture-
of-distribution hypothesis. Econometrica 44:305-21.
Gallo, G. M., and B. Pacini. 2000. The effects of trading activity on market
volatility. The European Journal of Finance 6:163-75.
Glaser, M., and M. Weber. 2004. Which past returns affect trading volume?
Working Paper, University of Mannheim.
Glosten, L. R., R. Jagannathan, and D. E. Runkle. 1993. On the relation
between the expected value and the volatility of the nominal excess
return on stocks. Journal of Finance 48:1779-801.
Granger, C. 1969. Investigating causal relations by economic models and
cross-spectral methods. Econometrica 37:424-38.
Hiemstra, C., and J. D. Jones. 1994. Testing for linear and nonlinear
Granger causality in the stock price-volume relation. Journal of Fi-
nance 49:1639-64.
Karpoff, J. M. 1987. The relation between price changes and trading
volume: A survey. Journal of Financial and Quantitative Analysis
22:109-26.
Lamoureux, C. G., and W. D. Lastrapes. 1990. Heteroscedasticity in stock
return data: Volume versus garch effects. Journal of Finance 45:221-9.
Lamoureux, C. G., and W. D. Lastrapes. 1994. Endogenous trading vol-
ume and momentum in stock-return volatility. Journal of Business and
Economic Statistics 12:253-60.
Lee, B.-S., and O. M. Rui. 2002. The dynamic relationship between stock
returns and trading volume: Domestic and cross-country evidence.
Journal of Banking and Finance 26:51-78.
Omran, M. F., and E. McKenzie. 2000. Heteroscedasticity in stock returns
data revisited: Volume versus garch effects. Applied Financial Eco-
nomics 10:553-60.
Suominen, M. 2001. Trading volume and information revelation in stock
markets. Journal of Financial and Quantitative Analysis 36:545-65.
Tauchen, G., and M. Pitts. 1983. The price variability-volume relationship
on speculative markets. Econometrica 51:485-505.
Tkac, P. A. 1999. A trading volume benchmark: Theory and evidence. Jour-
nal of Financial and Quantitative Analysis 34:89-114.
Human Capital and Economic Growth
by Municipalities in Slovenia
Matjaž Novak
Štefan Bojnec
This article presents the analysis of the nature of economic growth
of the Slovenian economy at the aggregate level and at the level of
Slovenian municipalities for the period 1996-2002. The aggregate cross-
sectoral time series dataset and the regional cross-sectional time series
dataset are used to econometrically test the significance of labour real-
location between sectors and municipalities on the nature of economic
growth of the Slovenian economy. For this purpose we compare esti-
mates of average and marginal stochastic frontier production functions.
The estimated parameters of these two groups of production functions
clearly indicate an inefficient use of human capital in the Slovenian
economy during the analysed period. The uncompleted process of sec-
toral labour reallocation is found as the main factor that has a negative
impact on the growth of total factor productivity in the Slovenian econ-
omy.
Key Words: economic growth, sectoral reallocation of labour,
total factor productivity, stochastic frontier model
jel Classification: o15, ö40
Introduction
Previous studies of the economic performance and growth of the Slove-
nian economy during the transition from a socialist to a market economy
raise an interesting theoretical and empirical question regarding the role
of human capital in these processes. Orazem and Vodopivec (1995) de-
scribed winner and loser associations through prevalence of winners' re-
turns to education and to a lesser extent to experience. Bojnec and Kon-
ings (1999) conducted an analysis on the magnitude and dynamics of job
Matjaž Novak is an Assistant Lecturer at the Faculty of Management
Koper, University of Primorska, Slovenia.
Dr Štefan Bojnec is Associate Professor at the Faculty of Management
Koper, University of Primorska, Slovenia.
This paper is based on a presentation originally given at the
5th International Conference of the Faculty of Management Koper
in Portorož, Slovenia, 18-20 November 2004.
creation and job destruction at the micro-level using a sample of Slove-
nian firms and compared the results with some other transition coun-
tries. Bojnec et al. (2003) found that human capital plays a crucial role
in the intersectoral labour mobility among agriculture, industry and ser-
vices. Bojnec (2003) found that regional location with associated eco-
nomic and human capital structures is an important factor that causes
differences in the level of economic development by statistical regions in
Slovenia. Novak (2004) found a significant contribution of human cap-
ital to the aggregate economic growth in Slovenia, but with a negative
influence of human capital on the growth of total factor productivity.
In this article we present the analysis of the nature of economic growth
of the Slovenian economy at the aggregate level and at the level of Slove-
nian municipalities for the period 1996-2002. The research was con-
ducted on the basis of the aggregate cross-sectoral time series data. Ad-
ditionally, we test the significance of labour reallocation on the nature
of economic growth of the Slovenian economy using the regional cross-
sectional time series data. The in-depth analysis at the municipality level
was necessary to obtain a sufficient number of observations for testing
statistical-significance of the parameters associated with labour realloca-
tion.
For the period 1996-2002, each time series dataset offers only 7 ob-
servations. The disaggregated dataset offers observations for each anal-
ysed variable by 174 Slovenian municipalities. This provides an appropri-
ate database for robust statistical estimations. The disaggregated dataset
by municipalities enables us to investigate the characteristics of the eco-
nomic growth in Slovenia during the second stage of transition. For this
purpose we use the stochastic frontier production function and the aver-
age production function framework.
We draw the following conclusions: first, human capital brings an im-
portant contribution to the aggregate growth; second, the uncompleted
process of sectoral labour reallocation is found as the main factor that
limits the contribution of human capital to the growth of total factor
productivity in the Slovenian economy; third, the comparison between
the estimated parameters of the stochastic frontier production function
and the average production function clearly indicates an inefficient use
of human capital in the Slovenian economy.
The following section briefly introduces a theoretical background of
the role of human capital in economic growth. We then present the
methodology used for analysing the role of human capital to the nature
of economic growth in Slovenia between the years 1996 and 2002. The
final section concludes with the main findings.
Theoretical Background on the Role of Human Capital
in Economic Growth
Human capital is defined as a factor of economic growth, which cap-
tures the abilities, skills and knowledge of workers (Romer 1994). It plays
a dual role in the process of economic growth. First, it is a factor of
production, and second, it is a source of innovation (Mincer, 1989, 1).
The human capital literature is dichotomised between two basic frame-
works: that of Becker (1964) and that of Lucas (1988). They emphasize
human capital as an alternative source of sustained growth (similar to the
technological progress). Second, there is Schumpeter's growth literature,
which is based on the work of Nelson and Phelps (1966). This stream of
literature highlights the importance of human capital stock (and not its
accumulation) for economic growth.
Regardless of which theoretical framework is used, human capital can
be regarded as a production factor and can be simply built into the model
of economic growth. The most popular in empirical literature on human
capital and economic growth in advanced market economies are growth
regressions proposed by Barro and Sala-i-Martin (1995), empirical anal-
ysis conducted by Mankiw et al. (1992), and researches by Benhabin and
Spiegel (1994). There exists also a body of literature and empirical anal-
ysis on the role of human capital in transition countries. Conventional
wisdom holds that transition countries are well endowed with human
capital, which is consistent with the main findings by Barro and Lee
(2001). They emphasised that most human capital indicators are better
placed in transition countries than in oecd countries, but on the con-
trary Boeri (2000) argued that the skills acquired in transition economies
are over specialised, lowering labour force mobility across industries and
consequently impeding economic progress.
The worker's mobility across industries plays a crucial role in former
transition economies. The resource allocation during the old system was
not based on market principles. Under different distortions and redis-
tributions, the great majority of workers were employed in state-owned
enterprises. When the transition process began, the market forces were
allowed to determine the economic activity. With economic liberalisa-
tion and deregulation of economic activities structural adjustment poli-
cies were introduced that induced structural changes and adjustments.
Different capital structures have moved from less productive industries
towards more productive ones. As we present further, labour force ad-
justments in Slovenia seem to be slow, thus reducing the speed of pro-
ductivity growth. As long as a predominant proportion of workers are
employed in less productive industries and not in more productive ones,
the aggregate growth of productivity will stay below the level that hinders
the economic development.
Since the productivity growth is a crucial factor of international com-
petitiveness, the hypothesis that sectoral reallocation of labour force in
transition economies is the key factor of progress and economic growth,
is plausible. We limit our empirical analysis to the Slovenian economy.
Methodological Framework
For estimating the impact of labour force movements between industries
the McCombie's (1980) methodological framework and, alternatively, the
econometric estimation approach of elasticity coefficients are the most
commonly used frameworks.
mccombie's decomposition method
McCombie (1980,104-106) developed an original framework for quan-
tifying the impact of sectoral reallocation of workers from low towards
high productivity industries on the growth of total factor productivity.
The starting point of McCombie's decomposition method is the calcula-
tion of the average labour productivity growth rate at the aggregate level:
where p denotes the growth rate coefficient of labour productivity, PT
denotes the aggregate level of labour productivity in the terminal year,
Po denotes the aggregate level of labour productivity in the base year,
while T denotes the terminal year.
The level of labour productivity at time t for the whole economy is
calculated as:
where P, Q, E are the levels of labour productivity, output and employ-
ment, i denotes the industry and a denotes the share of industry is
employment in total employment. From definition (2) it follows that
the aggregate productivity depends on two different factors. First, on
(1)
(2)
the industry's specific productivity of labour, and second, on the num-
ber of workers employed in each industry. Hence the growth of the
employment-share in industries with a higher productivity level will
raise the aggregate productivity, and vice versa will lower it in industries
with a lower productivity level. The labour movement between indus-
tries is only one factor that influences the aggregate productivity. This
impact, according to McCombie (1980), is described as the structural
component. The impact of all other factors is described as the standard-
ized component. The evaluation of these two components derives from
expression (1). Taking the natural logarithm of (1) and considering (2)
we get:
ln(p) = In I
ln
^ Pi,T • ai,T
i=i
ln
^ Pi,0 • a,0
. i=i
(3)
For estimating the standardized component of the aggregate produc-
tivity growth we extract from (3) the impact of the structural changes
and introduce the assumption ai,0 = Ui>T:
ln
^ Pi,T • ai,0
i=i
= p*,
(4)
- ln ^ Pi,0 • ai,0
i=i
where p* denotes the standardized component of the aggregate labour
productivity.
If we subtract the standardized component of the aggregate produc-
tivity (4) from the aggregate productivity (3), we obtain the structural
component (5) as follows:
ln
^ Pi,T • ai,T
i=i
ln
^ Pi,0 • at,0
i=i
ln
^ Pi,T • ai,0
i=i
ln
^ Pi,0 • at,0
i=i
ln
^ Pi,T • ai,T
i=i
ln
^ Pi,0 • at,0
i=i
ln
^ Pi,T • ai,0
i=i
+ ln
^ Pi,0 • ai,0
i=i
n
n
n
n
n
ln
1=i
^ i,T • ai,T
ln
^ Pi,T • aio
1=i
= p**,	(5)
where p** denotes the structural component of the aggregate productiv-
ity growth.
econometric framework
Within an econometric approach, the estimates of the elasticity model
are commonly used for measuring the impact of structural adjustment
processes on the growth of separate economic variables. More specifi-
cally, we are trying to quantify the extent of labour movements from less
productive industries towards more productive ones and their impacts
on the growth of aggregate productivity in order to investigate the mag-
nitude of a 1% increase in these movements. Novak (2004) developed a
convenient framework for conducting this kind of analysis. His frame-
work contains three separate parts: first, the correlation analysis; second,
the estimation of a logit model; and third, the estimation of the elasticity
model.
The basic idea is to estimate the following model:
y2 = fX),	(6)
where y2 denotes the contribution of the total factor productivity to the
economic growth and x3 the extent of sectoral reallocation of labour
force towards productive industries. The variable x3 is calculated as the
difference between the share size of workers employed in productive in-
dustries in the terminal year and the share size of workers employed in
productive industries in the base year (see next section). The greater and
more positive this difference, the greater is the process of structural ad-
justment in the period between the terminal and the base year. The equa-
tion (6) can be specified as an ordinary elasticity model (7):
y2 = aoXTexp(e)/ln ^ ln(y2) = ln(ao) + ^lnfe] + e,	(7)
or as the logit model (8):
/ P(y3 = l|x3) \
Using the econometric framework, we are faced with a specific prob-
lem. We need a sufficient number of observations for dependent and
explanatory variables. Namely, we acquire a combination of data on the
n
n
contribution of total factor productivity to the growth using the esti-
mates of production function and those of the growth accounting frame-
work, which are further explained. But the time period 1996-2003 pro-
vides only 7 observations. Hence, the time series data are not appropriate
even for the aggregate production function estimates.
The problem can be resolved by introducing another dimension in
our analysis, i. e. the observation at the municipality level. If we combine
the sectoral dimension (about 30 industries according to nace classifi-
cation) with the period of seven years, the panel data framework can be
established to estimate production functions at the municipality level.
This procedure can assure a sufficient number of observations for the
dependent variable (the contribution of the total factor productivity to
the economic growth) and the explanatory variable (the amount of sec-
toral reallocation of labour force towards more productive industries).
Data Used
We employ the proposed econometric framework as well as McCom-
bie's framework. The methodological details and belonging empirical es-
timates are discussed in the following section.
Within the econometric framework we use a three-stage procedure.
First, we estimate production functions at the level of municipalities.
Real value added expressed in 1996 constant prices is used as the depen-
dent variable, while the producer price index (PPI) acts as a deflator,
where PPI1996 = 100. As the first explanatory variable we use the vari-
able of the effective labour force that was calculated according to Barro
in Lee's (1994) methodological framework:
x1 = HKI • L, where	(9)
where the symbols mean:
x1 - variable that measures the amount of human capital expressed
in terms of effective labour force used for production,
HKI - human capital index,
L - labour force expressed as number of employees,
Wj - coefficient of relative real wage for j-th level of acquired
education,
Kj - share of employed people (labour force) with j-th level of
acquired education.
k
(10)
As the second explanatory variable we use the amount of capital as a
production factor. This variable is expressed in terms of tangible fixed
assets, and is also expressed in 1996 constant prices.
At the second stage we estimate the contribution of each separate pro-
duction factor (physical capital, human capital and total factor produc-
tivity) to past growth.
At the third stage we estimate the elasticity model and logit model.
In the case of the elasticity model the dependent variable measures the
contribution of total factor productivity to economic growth (we have
147 estimates on this variable since we provide estimates of the produc-
tion function for 147 municipalities). The explanatory variable measures
the amount of sectoral reallocation of labour force from less productive
industries towards more productive ones and was calculated as follows:
X3 = 02002 - Oi996, where	(11)
LP 1996
01996 = —-and	(12)
LD1996
LP2002
2002 - 77;-•	U3J
LD2002
Symbols:
x- - variable that measures the sectoral labour reallocation expressed
as the change in the share of labour force employed in the propulsive
industries with respect to labour force employed in the digressive
industries,
02002 - variable that measures the share of labour force employed in
the propulsive industries with respect to labour force employed in
the digressive industries in the year 2002,
01996 - variable that measures the share of labour force employed in
the propulsive industries with respect to labour force employed in
the digressive industries in the year 1996,
LP1996 - variable that measures labour force employed in
the propulsive industries in the year 1996,
LD1996 - variable that measures labour force employed in
the digressive industries in the year 1996,
LP2002 - variable that measures labour force employed in
the digressive industries in the year 2002,
LD1996 - variable that measures labour force employed in
the propulsive industries in the year 2002.
For estimating the logit model we take the same explanatory variable
as in the case of the ordinary elasticity model, while the dependent vari-
able takes value 1 if the contribution of total factor productivity to eco-
nomic growth was more than 50% and 0 if it was less than 50%.
All needed data for conducting the empirical estimates were acquired
from the Statistical Office of the Republic of Slovenia.
Empirical Framework
estimation of average and stochastic frontier
production functions
The role of human capital and the nature of economic growth are de-
rived from the comparison of the estimated production function coef-
ficients, particularly the elasticity of output pertaining to human capi-
tal. However, there exist two different production function frameworks,
which are used for economic analysis: first, the average production func-
tion framework and second, the marginal stochastic frontier production
function framework. The advantage of the stochastic frontier model is
that it considers inefficiency and random disturbances and can therefore
explain why production at a certain moment in time is not at the tech-
nological frontier. On the other hand, the average production function
approach assumes that production is at the technological frontier. Hence,
this approach does not distinguish between technological progress and
efficiency gains to explain why total factor productivity is changing. This
difference can be used for detecting possible inefficiency in production.
Namely, if there exists a large difference between estimated coefficients
of the stochastic frontier production function and aggregate production
function, this means that production factors are not used efficiently. To
answer this question we estimate the aggregate production function as
defined in equation (9). First, we estimate it as the average production
function using the convenient ordinary least square (ols) estimator for
panel data. Second, we estimate the same model as the marginal stochas-
tic frontier production function.
where the symbols mean:
y - variable that measures the amount of produced output,
ß0 - constant term that expresses the level of total factor
productivity,
x1 - variable that measures the amount of used production factor
human capital,
(14)
ß1 - coefficient of elasticity,
x2 - variable that measures the amount of used production factor
physical capital,
ß2 - coefficient of elasticity,
s - error term.
The stochastic production frontier models were first introduced by
Aigner et al. (1977) and Meeusen and van den Broeck (1977). The nature
of the stochastic frontier production function can be best derived from
the average production function model (such as in equation 14) that is
appropriate only for economies without inefficiency. A fundamental el-
ement of the stochastic frontier production function is that an economy
produces less than it might due to inefficiency. The production function
that considers this standpoint is specified as follows:
where the symbols mean:
y - variable that measures the amount of produced output,
ß0 - constant term that expresses the level of total factor
productivity,
x1 - variable that measures the amount of used production factor
human capital,
ß1 - coefficient of elasticity,
x2 - variable that measures the amount of used production factor
physical capital,
ß2 - coefficient of elasticity,
s - error term.
6 - term of technical inefficiency.
The value for 6 must be in an interval (0,1]. If 6 = 1, then the economy
is achieving the maximum output with the technology embodied in the
production function (see equation (15)). Since output is assumed to be
strictly positive, the degree of technical efficiency is also assumed to be
strictly positive.
Taking the natural logarithms of equation (15) and defining we get:
Note: Definitions of symbols are reported in equation (15).
Since u is subtracted from ln(y) the restriction 0 <6 < 1 implies that
u > 0. For estimating the parameters of the stochastic frontier produc-
tion model (and also the average production function with the ols esti-
mator) the statistical package Stata 8 is used in calculations that provide
(15)
ln(y) = [ln(ß0) + ß1ln(x1) + ß2ln(x2) + s] - u.
(16)
table 1 Econometric estimates of aggregate average and aggregate marginal
stochastic frontier production functions
	(1)	(2)	(3)
£yi ,xi	0.507	0.321	0.662
syi ,x2	0.312	0.501	0.149
ßo	3.876	4.232	2.661
ey 1,X1 + ey\,x2	0.819	0.822	0.811
Note: Column headings as follows: (1) aggregate average production function,
(2) aggregate marginal stochastic frontier production function, (3) aggregate average
production function.
£yi ,x\ - coefficient of elasticity of output pertaining to human capital,
Syl ,x2 - coefficient of elasticity of output pertaining to physical capital,
ß0 - constant term.
Source: Novak 2003.
a Maximum-likelihood estimator for a time-invariant, time-varying de-
cay stochastic frontier production function model, and for a truncated-
normal random variable u ~ N+(ß, afy.
The estimates are presented in table 1. The first column shows esti-
mates of the average production function using the ols estimator while
the second column gives estimates of the marginal stochastic frontier
production function using the Maximum-likelihood estimator for the
time invariant model.
The comparison of results of the estimated average and stochastic
frontier production function does not indicate any large differences. We
could make an assertion that persistent differences are due to different es-
timators used. But of special interest are ratios of estimated parameters.
In the average production function, the estimated parameters pertaining
to human capital are in both cases higher than the estimated parame-
ters pertaining to physical capital. Yet, the estimated parameters of the
marginal stochastic frontier aggregate production function exhibit op-
posite values. The estimated parameter pertaining to physical capital is
greater than the estimated parameter pertaining to human capital.
The differences detected between the two estimates are quite impor-
tant from an economic point of view. We are faced with two different
measures of economic policy, the objective of which is to achieve a faster
economic growth. If our starting points are estimates of the average pro-
duction function we will support the growth of human capital. The in-
crease of human capital by 1% is associated with the increase of output by
0.507%, whereby the increase of physical capital by 1% is associated with
the increase of output by only 0.312%. But if our starting points are esti-
mates of the aggregate stochastic frontier production function the advice
for policy makers will be the opposite. In this case the increase of physical
capital will be more appropriate as it would produce a higher economic
growth. The increase of physical capital by 1% is associated with the in-
crease of output by 0.501%, whereby the increase of human capital by 1%
is associated with the increase of output by only 0.321%. An interesting
feature of the results is also decreasing returns to scale in both production
function models (in the average and in the marginal stochastic frontier).
This swap of estimated coefficients that is conditional on the selected
framework of the production function suggests an inefficient use of one
or both production factors. Foundations for this statement arise from
the methodological features of the marginal stochastic frontier model
compared with the average production function. As we have highlighted,
there is no distinction between technological progress and technical effi-
ciency within the average production function framework. It is assumed
that production factors are used efficiently. As we know, this is not the
case within the framework of the stochastic frontier production function
that permits also inefficiency.
The existence of inefficiency is demonstrated by the distance of the
actual production from the production frontier. The increasing ineffi-
ciency reduces the value of the estimated elasticity coefficients of output
pertaining to the production factor that is used inefficiently. In our case
the highest value of the coefficient of elasticity of human capital is sig-
nificant in the average production function framework that postulates
its efficient use. This coefficient is lower than is the relevant coefficient
of elasticity, which is estimated within the stochastic frontier framework
suggesting the existence of inefficiency. Therefore, we confirm that hu-
man capital is the production factor that is used inefficiently in the Slove-
nian economy. We therefore conduct the growth accounting analytical
framework, which is based on the estimated parameters of the average
aggregate production function and the stochastic frontier aggregate pro-
duction function. Results are summarised in table 2.
As we can see from the results, the contribution of physical capital
to economic growth (approximately 56%) remains constant regardless
of the production function framework used. This is obviously not the
case for the contribution of human capital to economic growth, which
is significantly lower than within the stochastic frontier framework. This
table 2 Estimates of growth accounting model
	(1)	(2)	(3)
ö	25.27	25.52	28.87
Y	56.67	56.72	56.04
72	18.06	19.76	15.09
Note: Column headings as follows: (1) aggregate average production function,
(2) aggregate average production function, (3) aggregate average production function.
ö - contribution of human capital to economic growth in %,
Y - contribution of physical capital to economic growth in %,
y2 - contribution of total factor productivity to economic growth in %.
Source: Novak 2003.
indicates that there exists a potential for a more efficient use of human
capital that can increase its contribution to economic growth.
Structural and Standardised Component of Aggregate
Productivity Growth
From the comparison of the estimated parameters of the average and the
stochastic frontier production functions, and the related results from the
growth accounting equations we can conclude that during the period
1996-2002 human capital (as a production factor) was used inefficiently.
That was the main reason for the decreasing returns to scale at the aggre-
gate level.
This fact raises a question about the main reasons leading to the in-
efficient use of human capital in the Slovenian economy. Some results
from our earlier analysis (Novak 2003) indicated that this could be re-
lated to the uncompleted process of sectoral labour reallocation towards
more propulsive industries with a greater labour productivity in terms
of value-added per employee. As we found, one of the key characteristics
of structural adjustments that occurred in the Slovenian economy be-
tween the years 1996 and 2002 was only a marginal change in the labour
reallocation from less productive industries (decreasing industries) to-
wards more productive and propulsive ones. In 1996 about 61% of labour
was employed in industries with an average productivity that was lower
than the average productivity in the Slovenian economy as a whole. By
2002 this share fell to approximately 60%. The required deeper struc-
tural changes of labour reallocation and a sufficient adjustment were ob-
viously not made during the analysed period.
McCombie (1991, 70-85) argued that the uncompleted process of sec-
toral reallocation of labour could negatively affect the growth of aggre-
gate productivity, which is the main source of the intensive nature of eco-
nomic growth. We follow his methodology to decompose the growth rate
of aggregate productivity in the Slovenian economy during the period
1996-2002 into a structural component that measures the contribution
of sectoral reallocation of labour to the growth of aggregate productivity,
and into the standardised component that measures the contribution of
other factors to the growth of aggregate productivity using the following
fundamental equation (McCombie 1991, 74):
The standardised growth component is defined as the aggregate pro-
ductivity growth that would have occurred if all sectors had experienced
the same growth rate of employment, i. e. if their employment had grown
at the same rate as that of the total employment. This standardised com-
ponent is expressed in the first square brackets. The structural compo-
nent of the aggregate productivity growth is caused by the labour real-
location from less productive industries towards more propulsive ones,
which is leading to changes in the sectoral structure of employment in
the national economy.
According to nace propulsive sectors, i. e. industries with labour pro-
ductivity that is greater than the average labour productivity in the whole
economy, are: ca Mining and quarrying of energy materials, cb Mining
and quarrying of non energy materials, d e Manufacturing of paper, pub-
lishing and printing, d g Manufacturing of chemicals products and man-
made fibres, e Electricity, gas and water supply, I Transport, storage and
communication, j Financial intermediation, k Real estate, renting and
business activities, l Public administration and defence, M Education, n
Health and social work, and o Other social and personal services. Note
that the results can be biased to government policies and associated pol-
icy transfers that had been in place at a time prior to Slovenia's accession
to the European Union (eu).
Digressive (or declining, lagging behind) industries are those experi-
encing a labour productivity which is lower than the average productivity
of the whole economy.
(17)
table 3 Calculation of the standardized and structural components of the aggregate
productivity growth in the Slovenian economy between the years
1996 and 2002
lnZ ,P,,t • a,,0	ln£ ,P,,0 • a,,0	ln£ ,P,,t • a,,t	T
8.12595	7-44173	8.10703	7
Data needed for calculating the standardized and structural compo-
nents of the aggregate productivity growth in the Slovenian economy are
summarised in table 3.
The first column shows the natural log of aggregate productivity in
2002 (terminal year) under the assumption that the sectoral structure of
labour is the same as in 1996 (base year); the second column gives the
natural log of aggregate productivity in the base year, while the third
column shows the natural log of aggregate productivity in the terminal
year. The last column represents the value of the terminal year. Using
these data we calculate the structural and standardized components of
the aggregate productivity growth as follows:
(18)
P = Pst + Ps
1
T
1
+ —
T
ln
Z P
i,T • ai,0
ln
^ Pi,0 • a,0
ln
Pi,
i,T • Oi,T
ln
Pi,
i,T • ai,0
(19)
i • [8. 12595 - 7.44173] + i[8. 10703 - 8.12595]
= 1. 09971,
where the symbols mean:
pst - the structural component of the aggregate productivity growth,
ps - the standardised component of the aggregate productivity growth.
Source: Own calculations.
The results support our hypothesis on the deterioration in the sectoral
structure of labour in the Slovenian economy during the period 1996-
2002. This is revealed in particular by the negative contribution of the
structural change of labour to the aggregate factor productivity growth.
On the basis of the empirical results of the estimated average and
stochastic frontier production functions, extended by the growth ac-
counting framework and the standardised and structural component of
the aggregate productivity growth, we can now explain the nature and
causes of the economic growth of the Slovenian economy between the
years 1996 and 2002. Extensive economic growth was characterised by
decreasing returns to scale, which caused an inefficient use of human
capital. The main reason for this inefficient use was the uncompleted
process of sectoral labour reallocations. We can clearly confirm that the
labour force with the embodied technological knowledge (i. e. human
capital) remains inefficiently allocated across industries.
Impact of Sectoral Labour Reallocation on the Nature
of Economic Growth
We finally discuss the significance of the impact of sectoral labour re-
allocation on the nature of economic growth. For conducting this test
we need a sufficient number of observations for the variable expressing
the nature of economic growth and for the variable expressing labour
reallocation towards propulsive industries. For satisfying this criterion
we extended our empirical analysis from the cross-sectoral time series
analysis to the regional cross-sectoral time series analysis. Hence we esti-
mated the stochastic frontier production functions together with the re-
lated growth accounting equations for 147 Slovenian municipalities. On
this basis we calculated a coefficient of the sectoral labour reallocation
for each Slovenian municipality.
Our objective is to explain the nature of the Slovenian economic
growth during the analysed period. We are trying to find out if there
exists any significant impact of labour reallocation across industries on
the extensive nature of economic growth in the Slovenian economy. We
use estimates of the correlation coefficient, the coefficient of elasticity,
and odds ratios from the logit model. The theoretical specifications used
in the empirical investigation are presented below.
Coefficient of correlation
£[(X3 - X3)(y2 - y2)]	. ,
r =------(20)
(n - 1)0x3 <2
Elasticity model
y2 = aoxaiexp(e)/ln ^ ln(y2) = ln(ao) + ailn[x3] + e	(21)
Logit model
( P(y3 = l|x3) ,
Human Capital and Economic Growth by Municipalities in Slovenia 173
table 4 Theoretical specifications of the coefficient of correlation, elasticity model
Source: Own calculations.
Symbols:
r - coefficient of correlation,
x3 - variable that measures the sectoral reallocation of labour,
x3 - average value of the variable x3,
y2 - variable that measures the nature of economic growth in terms of
the contribution of the total factor productivity to economic growth,
y2 - average value of the variable y2,
n - number of observations,
<x3 - standard deviation for variable x3,
<y2 - standard deviation for variable y2,
a0 - regression constant,
ai - coefficient of elasticity,
e - error term,
L - logit (logarithm of odds ratio),
y3 - binary dependent variable with value i if the nature of the
observed municipality's economic growth was intensive (i. e. the
contribution of total factor productivity exceeded 50%) or value 0
if the nature of economic growth of the selected municipality
was extensive (i. e. the contribution of physical and human capital
to economic growth together exceeded 50%),
P - probability that the nature of economic growth is intensive.
The estimates of the correlation coefficient, the coefficient of elasticity,
the logit and odds ratio are reported in table 4.
The coefficient of correlation indicates the medium linear relationship
between the contribution of total factor productivity and labour reallo-
cation towards propulsive industries (both variables are expressed in nat-
ural logarithms). The high statistically significant coefficient of elastic-
ity indicates that the percentage increase of labour reallocation towards
propulsive industries induces an increase of the contribution of total fac-
tor productivity by 0.542% (a relatively substantial influence). Statisti-
cally significant is also the estimated parameter of the logit model. The
and logit model
Coefficient of correlation
Coefficient of elasticity
Odds ratio
r = 0.45
al = 0.54 [p = 0.0000]
& = 2.287
odds ratio indicates the increase of the odds intensive growth by 2.287
times if the share of the labour force employed in propulsive industries
compared to the share of the labour force employed in the digressive in-
dustries rises by one percentage point.
Conclusions and Policy Implications
During transition to a market economy and the process of Slovenian ad-
justments to eu membership, the majority of the Slovenian economic
growth was determined by an extensive growth of labour and capital. We
have more specifically analysed the nature of economic growth in the
Slovenian economy in the period 1996-2002 in order to determine the
reasons for decreasing returns to scale. We have applied the average pro-
duction function and the stochastic frontier production function and es-
timated the parameters of the municipality production functions on the
basis of cross-sectional and time series data. Using these pooled econo-
metric approaches and the results obtained we developed growth ac-
counting equations for 147 Slovenian municipalities and thus estimated
the contributions of each particular production factor (physical capital,
human capital and total factor productivity) to the municipality out-
put growth. We have analysed the main factors that are important for
the economic growth and development of municipalities. The in-depth
econometric analysis at the municipality level was also necessary to ob-
tain a sufficient number of observations for testing statistical significance
of the parameters associated with labour reallocation.
We have econometrically tested the significance of the labour realloca-
tion process to the nature of economic growth in the Slovenian economy
using the municipality cross-sectional time series data. We found that
the main reason for decreasing returns to scale in the Slovenian econ-
omy in the period 1996-2002 was an inefficient use of human capital in
the production process. One of the main constraints responsible for this
inefficiency was the uncompleted structural labour reallocation from de-
creasing industries towards more propulsive ones. Empirical results of
the coefficient of correlation, the coefficient of elasticity and the odds
ratio of the estimated logit model clearly indicate that the reallocation
of labour towards propulsive industries has statistically significantly in-
fluenced the rise of total factor productivity. The labour force with the
embodied technological knowledge (i. e. human capital) remains ineffi-
ciently allocated across industries.
It is obvious that the uncompleted process of sectoral reallocation of
the labour force impedes the growth of total factor productivity. Hence,
to accelerate the economic growth those measures of economic policy
should be pursued that would accelerate the process of sectoral reallo-
cation of labour from low productive industries towards high produc-
tive ones. Following the classical economic theory, differences in relative
wages between industries should be the appropriate force to provide in-
centives for labour transfer from less productive industries (with lower
wages) towards more productive ones (with higher wages). This is an is-
sue for future research, which is also concerned with some other factors.
First, it should be emphasized that the migration between industries is
more likely to be concerned with the expected earnings over the working
life than with current wage differences. Second, migration of a worker
from one industry to another can be connected with the regional mi-
gration that raises the costs of migration. If expected costs exceeded the
expected benefits from higher wage, the labour reallocation would be
hindered and less likely to occur.
Structural changes in the economy are a constituent part of the eco-
nomic growth process, where labour movements across industries aim
to maximize lifetime incomes over the expected costs of movements.
The decision to migrate is likely to depend on an individual's confi-
dence in own abilities to respond to changes in the labour market. This
can depend on the number of different factors that are inherent to each
individual worker. The level of acquired education with embodied hu-
man, social and some other pertaining capitals are variables that express
the worker's confirmation and ability to move subject to the lifetime in-
come maximization. Additionally, there is the nature and the level of
acquired education. Overly specialized skills may impede an individual
from finding a job in other industries or may require additional efforts
and investments. He/she can improve his/her abilities and competitive-
ness in the labour market by participating in lifelong learning which is
becoming a common practice today. While in the short run economic
policy measures seem to persuade the worker to change the industry and
also migrate into another region, on the long run the education policy
is the key factor that improves knowledge and skills allowing each indi-
vidual to respond quickly to structural changes. Successful reallocation
of labour among industries as well as among regions is closely connected
with the individual's skills acquired during the period of active education
and permanent formal and informal lifelong learning.
References
Aigner, D., C. A. Lovell Knox, and P. Schmidt. 1977. Formulation and es-
timation of stochastic frontier production function models. Journal of
Econometric 6 (1): 21-37.
Barro R. J. and J. W. Lee. 1994. Sources of economic growth. Carnegie-
Rochester Conference Series on Public Policy 40:1-46.
Barro, R. J., and X. Salla-i-Martin. 1995. Economic growth. New York:
McGraw-Hill.
Barro, R. J., and J. W. Lee. 2001. International data on educational attain-
ment: Updates and implications. Oxford Economic Paper 53 (3): 541-63.
Becker, G. 1964. Human capital. New York: Columbia University Press.
Benhabib, J., and M. M. Spiegel. 1994. The role of human capital in eco-
nomic development: Evidence from aggregate cross-country data.
Journal of Monetary Economics 34 (2): 143-73.
Boeri, T. 2000. Structural change, welfare systems and labour reallocation.
Oxford: Oxford University Press.
Bojnec, Š. 2003. Labor market characteristics by sectors and statistical
regions in Slovenia and their policy relevance. Paper presented at
the xiii daes-öga Conference, 18-20 September, Rodica/Domžale,
Slovenia.
Bojnec, Š., and J. Konings. 1999. Job creation, job destruction and labour
demand in Slovenia. Comparative Economic Studies 41 (2-3): 135-49.
Bojnec, Š., L. Dries, and J. F. M. Swinnen. 2003. Human capital and la-
bor flows out of the agricultural sector: Evidence from Slovenia. Paper
presented at the xxv iaae Conference, 16-22 August, Durban, South
Africa.
Lucas, R. E. 1988. On the mechanism of economic development. Journal
of Monetary Economics 22 (1): 3-42.
Mankiw, N. G., D. Romer, and D. N. Weil. 1992. The growth of nations.
Brookings Papers on Economic Activity 25:275-310.
McCombie, J. S. L. 1980. On the quantitative importance of Kaldor's laws.
Bulletin of Economic Research 32:102-12.
McCombie, J. S. L. 1991. The productivity growth slowdown of the ad-
vanced countries and the inter-sectoral reallocation of labour. Aus-
tralian Economic Papers 30 (56): 70-85.
Meeusen W., and J. van der Broeck. 1977. Efficiency estimation from Cobb-
Douglas production function with composed error. International Eco-
nomic Review 18 (2): 435-44.
Mincer, J. 1989. Human capital responses to technological change in the
labour market. nber Working Paper 803.
Nelson, R., and E. Phelps. 1966. Investment in humans, technological dif-
fusion, and economic growth. American Economic Review 56:69-75.
Novak, M. 2003. Analysis of the nature of economic growth of the Slove-
nian economy. Managing Global Transitions 1 (2): 153-67.
Novak, M. 2004. The returns to education: Some empirical findings for
Slovenia. In Knowledge society - challenges to management: Globalisa-
tion, regionalism and eu enlargement process; Proceedings of the 4th
International Conference of the Faculty of Management Koper, 20-22
November 2003, ed. E. Žižmond, 51-67. Koper: Faculty ofManagement.
Orazem, P. F., and M. Vodopivec. 1995. Winners and losers in transition:
Returns to education, experience and gender in Slovenia. The World
Bank Economic Review 2:2001-230.
Romer, D. 1994. Advanced macroeconomics. Boston: McGraw-Hill.
Where is the Border Between an Information
System and a Knowledge Management System?
Imandra Galandere-Zile
Viktorija Vinogradova
Both information systems and knowledge management systems are im-
portant in an organisation and often fulfil the same task. The aim of this
paper is to identify the border between these two systems. The approach
of the research is to analyse both systems in order to identify their role
in an organisation, determine the problems, advantages, and opportu-
nities, and discover common and uncommon features between them
to find out when an information system becomes a knowledge man-
agement system and when it is useful to implement the latter.
Key words: information system, knowledge management system,
information, knowledge and knowledge management
jel Classification: M1, l86 in c88
1 Introduction
Nowadays information systems play a very important role in improv-
ing an organisation's performance and its increased competitive capac-
ity. Therefore, it is essential for organisations to decide what are the most
important business processes and core competencies that have to be sup-
ported by an information system, and what kind of information system
has to be implemented and conforms to the organisation's requirements.
While the latest trends in organisational development have demon-
strated the importance of knowledge management, there is no universal
definition of knowledge management or a knowledge management sys-
tem. Knowledge management systems are mostly built over existing in-
formation systems, thus it is difficult to determine when an information
system becomes a knowledge management system or what kind of fea-
Imandra Galandere-Zile is Director of the Quality Management
Department, The Treasury of the Republic of Latvia.
Viktorija Vinogradova is an Assistant at the Department of Systems
Theory and Design, Riga Technical University, Latvia.
This paper is based on a presentation originally given at the
5th International Conference of the Faculty of Management Koper
in Portorož, Slovenia, 18-20 November 2004.
tures are encompassed in a knowledge management system and not in
an information system.
The goal of the paper is to identify the border between information
systems and knowledge management systems, and discover common and
uncommon features between them in order to find out when an infor-
mation system becomes a knowledge management system and when it is
useful to implement the latter.
The paper is organised as follows. In section 2 data, information and
knowledge are analysed since they play an essential role in both systems.
Considerations regarding information systems and their role in an or-
ganisation are discussed in section 3. Section 4 outlines the area of knowl-
edge management and its role in an organisation. Section 5 focuses on
the relationship between both systems, and discusses the contribution of
information technology and systems to knowledge management. Com-
mon and uncommon features between these two systems are identified
and analysed in section 6. Finally, some brief conclusions and directions
for intended future work are provided.
2 Data, Information and Knowledge
Since data, information and knowledge play an essential role in both
an information system and a knowledge management system, they were
analysed first.
In practice, the terms data, information and knowledge are often used
interchangeably. First of all, the differentiation is generally difficult be-
cause both data and information are located in an information man-
agement system. Secondly, users interpret data only when they use them.
Users' contexts and the information manufacturing process make it diffi-
cult for users to determine precisely whether a piece is considered as data,
information, or even knowledge. Although it might take a decade just to
determine what these three words mean, we all agree that the transfor-
mation of data for clear and more meaningful information to users is
important. Structuring and managing knowledge assets in an organisa-
tion are also critical for all organisations (Huang et al. 1999).
Data represent facts that are created when business processes are per-
formed. They form a set of particular and objective facts about an event
or simply the structured record of a transaction (Tiwana 2000). They are
also the lowest level of known facts. Data can be stored in a structured
relational database system or in an unstructured document management
system, and includes non-text information, such as voice and image.
Data are collected, stored, grouped, analysed, and interpreted (Huang
et al. 1999).
The terms data and information are often used synonymously (like in-
formation and knowledge). In practice, managers differentiate between
information and data intuitively and describe information as data that
has been processed. Information contains substance and purpose. There
are different ways in which meaning can be added to data in order to
transform it into information. Data becomes information through con-
densation, contextualisation, calculation, categorisation and/or correc-
tion processes (Tiwana 2000). What qualifies as useful information is a
subjective judgment. Information moves around in both electronic and
hard format, through electronic and social networks.
The key link between knowledge and information is probably best ex-
pressed in the commonly accepted idea that knowledge in the business
context is nothing but actionable information. Knowledge is generated
when information is combined with context and experience. Knowledge
is a fluid mix of framed experience, values, contextual information, ex-
pert insight and grounded intuition that provides an environment and
framework for evaluating and incorporating new experience and infor-
mation. It originates and is applied in the minds of those in possession
of the relevant knowledge. In organisations it often becomes embedded
not only in documents or repositories but also in organisational rou-
tines, processes, practices and norms (Tiwana 2000). We strongly agree
with Tiwana who states that data and information are essential, but it is
the knowledge that can be applied, experience that comes into context,
and skills that are used at that moment that make the difference between
a good and a bad decision.
3 Considerations on the Information System
and its Role in an Organisation
This section presents a short overview of information systems and the
benefits that can be acquired by implementing and applying such sys-
tems.
Information systems are widely used in organisations. They provide
information and thus help improve an organization's operation and
management. An information system can be defined as a group of com-
ponents working together. These components include equipment (or
hardware), instructions (or software), data stored in the system, the peo-
ple to operate the system, and procedures for the people to follow (Nick-
erson 1998). Because information processing can be done using manual
instructions, a wider view on information systems is also possible. Thus a
computerised information system is just one of the information systems.
In this paper we consider only computerised information systems.
An information system supports one or more work systems using in-
formation technology to capture, transmit, store, retrieve, manipulate or
display information (Alter 1999).
A work system is a system in which human participants perform busi-
ness processes using information, technology, and other resources to
produce products for internal or external customers. The core of the
work system (and thus the information system) is a business process con-
sisting of steps related in time and place, having a beginning and an end,
inputs and outputs.
There are a wide variety of different information systems that can be
classified by:
•	The number of users - individual, workgroup, organisational and
inter-organisational.
•	Ways users are connected to information technology - directly,
through a network, through the Internet.
•	Supported business functions - accounting, transaction, manufac-
turing etc.
Business processes are at the core of every information system. In-
formation systems support business processes for one or more business
functions. The simplest information system supports only one business
function (for e. g. inventory systems, payroll systems etc.), in other cases
information systems support a number of business functions. In these
cases the information is transmitted automatically, thus saving time and
work that would be necessary to maintain the integrity of these func-
tions in an organisation. These information systems can thus be called
integrated.
Information systems provide several benefits to the organisation (Nick-
erson 1998). One of the main benefits is better information. Information
systems not only store and process data, they also produce information,
which is the basis for good decision-making. Better information is avail-
able if data are properly managed in the information system, i. e. the data
are available for processing, and are current, accurate, and secure (Alter
1999). Another benefit is better information. Information systems oper-
ate at any time of the day or night and process data faster than humans.
The third benefit is increased productivity. Information systems make the
work more productive in a shorter period of time.
4 Knowledge Management and its Role in an Organisation
While recent trends in organisational development have demonstrated
the importance of knowledge management, there is no universal defini-
tion of it. Besides, Wilson (2002) considers knowledge management (like
Business Process Reengineering) as a utopian ideal that cannot be suc-
cessfully implemented. Knowledge management addresses various fields
related to the organisation, people, motivation, and technology. The goal
of knowledge management is to increase the efficiency of activities re-
lated to knowledge as well as the benefits acquired from it. In order to
transform knowledge into a valuable organisational asset, knowledge,
experience, and expertise must be formalised, distributed, shared, and
applied (Galandere-Zile 2002).
The knowledge management cycle reflects the life cycle of corporate
knowledge. This dynamic process often starts with finding and collect-
ing internal knowledge and best practices. The second step is sharing
and understanding those practices so that they can be used. Finally, the
process includes adapting and applying such knowledge and practices to
new situations and bringing them up to the best practice performance
level. Knowledge management is concerned with supporting and opti-
mising these processes. Surrounding the process, and helping or hiding
it, are what we call the enablers. These are: leadership, culture, technol-
ogy and measurement. These aspects of an organisation's environment
and infrastructure must be addressed in order to transfer a business pro-
cess and develop knowledge management in an organisation (Zaharova
and Galandere-Zile 2002).
Knowledge management has come to be regarded as the driving force
behind some of the world's largest and most successful organisations. It is
seen as the next evolutionary step that goes beyond the 'Learning Organ-
isation' or 'Business Process Reengineering' and even beyond 'Group-
Ware' and the 'World Wide Web'. It is meant to integrate them all into
an even higher level, enterprise-wide framework with its new work roles,
responsibilities, reward systems, methods and tools, because an effective
knowledge management depends not merely on information technol-
ogy platforms but primarily on the social structure of an organization
(Phillips et al. 2004). In other words, true knowledge management con-
cerns radical and fundamentally new ways of creating, retaining, shar-
ing and leveraging knowledge about people and organisations; ways that
were simply not possible before.
5 State of the Art Concerning Knowledge Management Systems
and Information Systems for Knowledge Management
This section focuses on the relationship between knowledge manage-
ment systems and information systems and discusses the contribution
of information technology and systems to knowledge management.
an information system and a knowledge
management system
Even though there is considerable disagreement in the literature and
business practice about what exactly knowledge management is, some
researchers and practitioners stress the importance and usefulness of
information and communication technologies as enablers or vehicles
for the implementation of these approaches. Knowledge management
systems should particularly help to overcome the shortcomings of cur-
rent practices of business engineering in regard to organisational perfor-
mance (Maier 2002).
Technology by itself does not constitute a knowledge management
program it rather facilitates one, especially in large, geographically dis-
persed organisations. Knowledge management represents an opportu-
nity to derive additional benefits from an organisation's existing invest-
ment in computers, databases and networks by integrating them to sup-
port knowledge management in many ways (Zaharova and Galandere-
Zile 2002). Technology's most valuable role in knowledge management
is broadening the reach and enhancing the speed of knowledge transfer
(Tiwana 2000).
Past management information systems basically used the computer as
a means of providing information to solve recurring operational prob-
lems. Today, there is a need for new types of systems that would focus
on discovering knowledge that responds to the changing environment.
By increasing the capabilities of decision makers, information systems
that support knowledge management initiatives help an organisation to
achieve its goals. Information systems that support information flow are
an essential component in a knowledge management system. Informa-
tion systems create a good virtual environment for knowledge manage-
ment (Galandere-Zile 2004).
Examples of information and communication technologies related to
knowledge management are (Maier 2002):
•	Intranet infrastructures that provide basic functionality for com-
munication (e-mail, teleconferencing) as well as storage, exchange,
search and retrieval of data and documents.
Document and content management systems that handle electronic
documents or Web content.
•	Workflow management systems that support well-structured organ-
isational processes and handle the execution of workflows.
•	Artificial intelligence technologies that support, for example, search
and retrieval, user profiling and matching of profiles, text and Web
mining.
•	Business intelligence tools that support the analytic process which
transforms fragmented organisational and competitive data into
goal-oriented 'knowledge' and require an integrated data base that
is usually provided by a data warehouse.
•	Visualisation tools that help to organise relationships between knowl-
edge, people and processes.
•	Groupware that supports, for example, time management, discus-
sions, meetings or creative workshops of workgroups and teams.
•	E-learning systems that offer specified learning content to employees
in an interactive way and thus support the teaching and/or learning
process.
Knowledge management systems promise to significantly enhance
functionality through an integrated combination of the aforementioned
information systems from the perspective of knowledge management.
A knowledge management system should not be seen as a voluminous
centralised database. It can rather be imagined as a large networked col-
lection of contextualised data and documents linked to directories of
people and skills and providing intelligence to analyse these documents,
links, employees' interests and behaviour, as well as advanced functions
for knowledge sharing and collaboration (Maier 2002).
The term knowledge management system has become a strong meta-
phor or vision for the development of a new breed of information and
communication technology systems. In this vision, the knowledge man-
agement system creates a corporate information and communication
technologies environment, a contextualised base, an infrastructure that
takes into account the complex nature of knowledge and thus supports
the handling of knowledge in organisations. In order to achieve this, a
number of heterogeneous information and communication technologies
have to be integrated, improved, recombined, and repacked. The devel-
opment of a knowledge management system is a complex undertaking
(Maier 2002).
Knowledge management has to handle and improve complex relation-
ships and networks rather than individual knowledge elements or just
one location, for e. g. a knowledge base. In the implementation process
of a knowledge management system the content to be managed is very
important. Companies that put organisational knowledge at the cen-
tre of consideration implement knowledge management. Typically, the
organisation's knowledge structure is determined in workshops or re-
flects sources that already exist in the organisation but are handled by
a number of incompatible information and communication technology
systems. In many cases, explicit knowledge is predominant. It is also a
lot harder to describe implicit knowledge that is an equally important
part of the organisational memory content (Maier 2002), though an im-
portant factor behind the implementation of knowledge management
systems is the ability of organisational personnel to share knowledge
through some type of online forum.
Knowledge management systems organise and make available an im-
portant know-how wherever and whenever it is needed. This centres
on 'best practices' or guiding principles, projected forecasts, reference
sources, proven processes and procedures, patent information, estab-
lished formulas, corrective fixes, and similar items. Data warehouses,
computer networks, company intranets, extranets, groupware, bulletin
boards, and video conferencing are the technological tools for storing
and distributing appropriate knowledge. Major components of knowl-
edge management systems are considered to be the best practices, com-
munication enablers, and system road maps. The main purpose of these
components is to provide users with the right information at the right
time and place. Experience has shown that successful knowledge man-
agement system developers are those with a well-developed collabora-
tion between all these tools (Maier 2002).
Knowledge management systems might also be viewed as important
organisational assets that provide core competencies for the organisa-
tion. In particular, highly knowledge-intensive organisations might view
the systematic handling of knowledge in general, and especially their in-
formation and communication technology systems supporting knowl-
edge management, as their core competence and fear that they might
lose a strategic advantage if they implement a standard software solution
available on the market (Thierauf 1999).
from an information system toward a knowledge
management system
The task of implementing a successful knowledge management system
may seem insurmountable. But in reality there are different views and
approaches to implementation of a knowledge management system.
Some experts have stated that up to 90 percent of the infrastructure
required for knowledge management is already in place. In most organ-
isations this refers to their existing structure of computer networks and
servers. Hence, only a small shift in the total computer technology bud-
get is required to make the change to a knowledge management system
(Lasker and Norton 1996). The success of an organisation is more depen-
dent on its capability to create an effective environment for knowledge
generation and application, and on the knowledge and talent it can re-
cruit, develop and retain in order to provide the innovation (Kim and
Mauborgne 1999). Knowledge work processes drive a successful knowl-
edge management system, not the technological issues. The rationale is
that these newer systems are helping decision makers make better deci-
sions in terms of their work activities. The technology is incidental to
this critical issue.
Redesign may be necessary for changing knowledge work processes
(Thierauf 1999):
•	The content of knowledge should be changed by expanding or con-
tracting what it encompasses in order to better meet desirable ob-
jectives.
•	The composition of work should be reordered (so that company per-
sonnel may change the focus of their jobs from information to
knowledge).
•	Change can be focused on the employment of new networking tech-
nology that lends itself to groupware.
Thierauf (1999) proposes a summary of those critical factors that de-
termine the appropriate software useful for providing knowledge for de-
cision makers and managing the software. For example:
•	Is the software easy to use and does it enable the development and
use of knowledge?
•	Does the software provide an in-depth analysis of patterns and
trends?
•	Does the software actually do what decision makers want here and
now?
•	Does the software allow decision makers to access and analyse a
large amount of information in order to get at pertinent knowl-
edge?
•	Is the software generally a combination of packages designed for the
organisation's size or will it be outgrown in a few years?
•	Does the software provide flexibility in the development of perti-
nent knowledge to meet decision makers' needs?
Knowledge management systems of today often integrate a variety of
off-the-shelf software products such as groupware, document manage-
ment systems, e-mail, relational databases, and workflow, with knowl-
edge extraction tools, knowledge management intranet search engines,
and knowledge discovery or the data mining software. In addition, a
knowledge management system can employ olap software, the statis-
tical analysis software to assist decision makers in getting the knowledge
at hand. Depending on the nature and purpose of a knowledge man-
agement system, there can be still other software products and hardware
needed to form a complete knowledge management system package to
gather, organise, collaborate, refine, and distribute knowledge (Thier-
auf 1999). Most organisations that had actually implemented knowledge
management system solutions have combined several tools and imple-
mented additional functions on their own rather than simply buying
specialised knowledge management system software on the market.
A classification of knowledge management systems can only be con-
sidered as preliminary due to the considerable dynamics of the market
for knowledge management. At this stage, the analysis of a knowledge
management system is a great challenge. This is already visible in the dif-
ficulties of defining the term and continues in the trial to present a typical
architecture of such systems or to give a comprehensive list of functions.
The same is true for a classification of knowledge management systems.
The pragmatic perspective that knowledge management systems are just
document management systems with some added functionality which
seems to dominate the market is unsatisfying. The information and com-
munication technologies support for knowledge management is not re-
stricted to the handling of documented knowledge (Maier 2002).
Information and communication technologies infrastructure needs a
strategy to define knowledge goals and subsequently implement organi-
sational instruments, roles and processes, and an organisational culture
supportive of knowledge sharing in order to create benefits for the or-
ganisation. There has been a shift in the perspective of knowledge man-
agement system vendors as well as organisations applying those systems.
The focus is now placed on the explicit side of knowledge management
rather than on a combination and integration of its implicit side.
6 Common and Uncommon Features of Information Systems
and Knowledge Management Systems
During the analysis of information systems and knowledge management
systems, common and uncommon features of both areas were ascer-
tained.
overview of common and uncommon features
of both systems
Knowledge management systems are operated on the basis of an (organi-
sation-wide) information and communication infrastructure, and in
most cases an Intranet platform of Lotus Notes environment on which
information sharing between (virtual) teams both within the organisa-
tion and across the organisational boundaries, and allies, suppliers and
customer is possible. Most organisations have installed a large number
of application systems and information and communication technol-
ogy platforms that provide functionality for knowledge management.
The basic functionality of such an information and communication
technologies platform designed 'with knowledge management in mind'
would comprise an integrated set of the following functions:
•	Communication: as well as coordination and cooperation, e. g. e-
mail, workflow management, newsgroup or list server.
•	Document management: handling documents throughout their life
cycle.
•	Access: to various data sources, e. g. relational data bases, document
bases, file servers or Web servers.
•	Search: basic search functionality.
•	Visualisation.
In a somewhat narrower sense, knowledge management systems pro-
vide functionality that goes well beyond these basic functions. Neverthe-
less there is no single hardware or software product or a combination
of the two that can provide a comprehensive approach to knowledge
work. Building a corporate-wide knowledge management infrastructure
requires the integration of many different technologies. It is necessary
not only to implement an integrated hardware or software technology
but also to integrate the company's personnel and their related business
processes with this knowledge management technology. If the company's
personnel are not working in a collaborative environment or if no pro-
cedures and processes are in place to share the knowledge, no amount
of knowledge management system technology can change that (Thierauf
1999). In other words, a knowledge management system requires a sys-
temic knowledge management initiative in order to be used effectively
and efficiently. This includes a knowledge management system strategy
and the development of knowledge management system goals, an appro-
priate organisational design describing roles responsible for knowledge-
related tasks and processes that use the knowledge management system, a
supportive organisational culture and a corresponding knowledge man-
agement system supervision that evaluates whether the goals of using
these systems have been achieved (Maier 2002).
The main differences between a knowledge management system and
more traditional information and communication technology systems,
such as document management systems, Intranet solutions or Group-
ware can be characterised as follows (Maier 2002):
•	A contextualised combination and integration of functionality.
•	An organisation-wide focus.
•	An integration of intelligent functions.
•	Matching with knowledge management initiatives.
•	Dynamics of organisational learning.
analysis of common and uncommon features
of both systems
The aim of this section is to provide a detailed analysis of an information
system and a knowledge management system.
As described in previous sections, information systems and knowl-
edge management systems have many common features. However, the
differences between them cannot be declared only by the system's name
or goals. We applied the Enterprise Knowledge Development (ekd)
methodology in order to analyse the features in which knowledge man-
agement systems differ from information systems.
Figure 1 describes the concepts that are essential for information and
knowledge management systems. Boxes that are in white colour denote
concepts that are related to both systems, boxes tinted in light grey repre-
sent concepts that are unique for knowledge management systems, while
dark grey boxes refer only to information systems.
Figure 1 shows that most of the concepts are common to both systems.
Although there are information systems that are implemented and main-
tained to support only one business process, knowledge management
systems support many integrated business processes as well as specific
knowledge related processes. Besides, the concept of knowledge is clearly
defined in knowledge management systems. It denotes that in the imple-
mentation process of a knowledge management system the content to be
managed is very important. Moreover, an efficient knowledge manage-
ment system requires knowledge management initiatives, namely, lead-
ership, measurement, and organisation culture.
Figure 2 describes the business processes of both systems. Boxes with
rounded corners that are shadowed denote external processes that are
performed by users themselves; other boxes are system processes accom-
plished by a computerised system, whether it is an information system
or a knowledge management system.
Specific processes of knowledge management are only concerned with
knowledge (creation, identification, sharing, formalisation, collection,
organisation, transfer, and, finally, application of knowledge). However,
it is essential to stress that internal processes of a knowledge management
system (processes 4, 5 and 6) use general information system processes
(1, 2 and 3). Thereby, during the performance of the knowledge man-
agement system's processes, explicit knowledge is transformed in some
kind of data or information that can be stored and processed by the in-
formation system. That confirms that a knowledge management system
is always supported by an information system.
During the analysis of information and knowledge management sys-
tems, the goals, opportunities (or benefits) and problems characteristic
of both systems were identified. One of the main goals of both systems is
to support one business process, i. e. decision making. In this process the
information system provides just better information, while the knowl-
edge management system provides intelligent decision making based on
Concept 7
Supporting many
business processes
Concept 6
Supporting one
business process
Concept 21
Knowledge related
processes
to
a
a
su
O
S"
3
Su
N
a
3
Su
3
3'
1
Su
figure i A concept model of an information system and a knowledge management system
£
g
I
ki
o
8
Rulel Change IS data to repr the current busines process state	esent s	
	Concept 9	
Ext. process 1
Capital 1
IS/KMS
s-
03
O
a.
to
figure 2 A business process model of an information system and a knowledge management system
vo
best practices, organisational knowledge and experience that nowadays
are very crucial for organisations that are confronted with increased en-
vironmental complexity and dynamics.
7 Conclusions and Further Research
The paper deals with issues concerning information systems and knowl-
edge management systems, and tries to identify the border between both
systems. The main conclusions derived from the analysis of both systems
are summarized below.
Data, information, and knowledge are essentially different and play
different roles in both information systems and knowledge management
systems, while in practice these terms are often used interchangeably and
knowledge is used as a synonym for information. As a result, knowledge
management systems are often considered as information systems under
another name (Wilson 2002).
Typically, the organisation's knowledge already exists within the or-
ganisation. Most of the explicit knowledge is handled by a number of
incompatible information systems that can serve as a platform that pro-
vides functionality for a knowledge management system. However, there
has been a shift in the perspective of organisations applying knowledge
management systems. The focus is now placed on the explicit side of
knowledge management rather than on a combination and integration
of its implicit side.
Knowledge management is a systematic and articulated approach
to managing knowledge related processes. It represents an opportu-
nity to derive additional benefits from the organisation's existing in-
vestment in computers, databases and networks by integrating them to
support knowledge management. Knowledge management has been im-
plemented by companies that put organisational knowledge at the centre
of consideration.
Knowledge management systems:
•	Have become a strong metaphor or vision for the development of a
new breed of information systems.
•	Organise and make available an important know-how wherever and
whenever it is needed.
•	Require a systemic knowledge management initiative in order to be
used effectively and efficiently.
•	Are operated on the basis of an (organisation-wide) information
and communication infrastructure. Technologies by themselves do
not constitute a knowledge management system, they rather facili-
tate one, and they are very important and useful as enablers for the
implementation of knowledge management approaches.
Due to the considerable dynamics of the market for knowledge man-
agement there are difficulties in presenting a typical architecture of
knowledge management systems or providing a comprehensive list of
functions.
The border between an information system and a knowledge manage-
ment system is diffused and depends on the existence of factors such as
an organisation's strategy and goals concerning business and knowledge
processes, culture, initiatives, information and communication tech-
nologies etc. An effective knowledge management depends not merely
on information technology platforms but primarily on the social struc-
ture of an organization. The knowledge management system focuses on
discovering knowledge that responds to the changing environment and
takes into account an implicit knowledge that plays an essential role in
an organisation's competitive advantage.
Future research is aimed at refining the architecture of knowledge
management systems and organisational information systems, and pro-
viding a deeper analysis of dependency relationships between both sys-
tems.
References
Alter, S. 1999. Information systems: A management perspective. 3rd edition.
Reading, ma: Addison-Wesley.
Galandere-Zile, I. 2002. Application of knowledge management elements
for quality management systems. In 11th International Conference on
Information Systems Development, Riga, eds. J. Stirna and K. Steins,
15-25. Riga: Riga Technical University.
Galandere-Zile, I. 2004. Applicability of erp systems for knowledge man-
agement in the context of quality management. In The 16th Interna-
tional Conference on Advanced Information Systems Engineering, Riga,
eds. A. Persson and J. Stirna, 276-89. Riga: Springer.
Huang, K.-T., Y. W. Lee, and R. Y. Wang. 1999. Quality information and
knowledge. Upper Saddle River, nj: Prentice Hall.
Kim, W. C., and R. Mauborgne. 1999. Strategy, value innovation, and the
knowledge economy. Sloan Management Review 40 (3): 41-54.
Lasker, H. H., and D. P. Norton. 1996. The new cio/ceo partnership.
Computerworld, 22 January.
Maier, R. 2002. Knowledge management systems: Information and commu-
nication technologies for knowledge management. Berlin: Springer.
Nickerson, R. C. 1998. Business and information systems. Upper Saddle
River, nj: Prentice Hall.
Phillips, F., L. Delcambre, and M. Weaver. 2004. Knowledge management:
A re-assessment and case. Knowledge Technology & Policy 17 (3/4): 65.
Tiwana, A. 2000. The knowledge management toolkit: Practical techniques
for building a knowledge management system. Upper Saddle River, nj:
Prentice Hall.
Thierauf, R. J. 1999. Knowledge management systems for business. London:
Quorum Books.
Wilson, T. D. 2002. The nonsense of 'knowledge management.' Informa-
tion Research: An international Electronic Journal 8 (1), paper no. 144.
Http://InformationR.net/ir/8-1/paper144.html.
Zaharova, S., and I. Galandere-Zile. 2002. The impact of the knowledge
management to business processes. In International Conference: Tra-
ditional and Innovative in the Lasting Development of Society, Rezekne,
Latvia, 298-304. Rezekne: Rezekne Higher Education Institution.
The Quality of Entrepreneurship Education
and the Intention to Continue Education:
Slovenia and Romania
Boštjan Antoncic
Cezar Scarlat
Barbara Hvalic Erzetic
Entrepreneurship and management education is important in develop-
ing knowledge and skills of entrepreneurs. In this paper, entrepreneur-
ship education satisfaction and quality are compared between two
countries: Slovenia and Romania, and the relationship between edu-
cation quality and education continuation is assessed in both coun-
tries. Multi-item measures were used, questionnaire data were obtained
in both countries, measurement scales were tested, and differences be-
tween the two countries were assessed by comparing means and regres-
sion analysis coefficients. We found that education content and pro-
cess quality and education satisfaction of participants tend to be the
strongest predictors of a subsequent decision to continue education in
both countries. Room and equipment adequacy may also be impor-
tant, but our findings showed this result only for Romania. Some other
findings and recommendations are also presented.
Key Words: entrepreneurship, education, quality
jel Classification: m10, m13
Introduction
The development of knowledge and skills of entrepreneurs can be to a
large extent dependent on entrepreneurship and management educa-
tion. In our time, characterized by the processes of globalization, the
rise of information and communication technology, and continuously
Dr Boštjan Antončič is Associate Professor of Entrepreneurship at the
Faculty of Management Koper, University of Primorska, Slovenia.
Dr Cezar Scarlat is Professor at the Department of Industrial
Management, University Politechnica of Bucharest, Romania.
Barbara Hvalič Erzetic is the ceo of Etna, d. o. o., Nova Gorica,
Slovenia.
This paper is based on a presentation originally given at the
5th International Conference of the Faculty of Management Koper
in Portorož, Slovenia, 18-20 November 2004.
changing business environments, the need for continuing education is
becoming even more important than before. Entrepreneurial training
significantly and positively impacts participants' perceptions of their
abilities to pursue and grow new ventures (Ehrlich et al. 2000). Knowl-
edge and skills acquired in entrepreneurship education can serve as a
motivational drive for creation of new ventures (Cho 1998). Education
and training can lead to development and improvement in some ele-
ments of entrepreneurship (Henry et al. 2003). Educational programs
can be important for the development of entrepreneurial talent within
the population (Rushing 1990). Education of entrepreneurs is becom-
ing increasingly needed for assuring a higher probability of new venture
success (Postigo et al. 2003). When deciding whether or not to continue
education, entrepreneurs may make their decisions depending on the
basis of their satisfaction or quality perception of past education. For en-
trepreneurship education success it is crucial to emphasize continuous
improvement in education quality (Han and Lee 1998). In this paper, we
compare entrepreneurship education satisfaction and quality between
two countries: Slovenia and Romania, and assess the relationship be-
tween education quality and education continuation in both countries.
Entrepreneurship education quality and continuation in countries with
a shorter entrepreneurial tradition and lower levels of entrepreneurship-
oriented culture, such as Slovenia and Romania, may have a stronger
potential new venture creation impact than in countries with longer en-
trepreneurial traditions, such as the us or Western European countries,
as suggested by Lee et al. (2005) in their comparative study of the impact
of entrepreneurship education in Korea and the us.
The focus of the paper is, hence, entrepreneurship education, which
can be defined as structured, formal conveyance of entrepreneurial
knowledge; entrepreneurial knowledge meaning the concepts, skills, and
mentality individual business owners use during the course of starting
and developing their growth-oriented businesses; and entrepreneurial
learning meaning the active and cognitive processes individuals employ
as they acquire, retain and use entrepreneurial knowledge (Young 1997).
Entrepreneurs, to be able to solve emerging problems and to increase
business results of their enterprises, need to learn continuously through
self-directed learning as well as through formal education and train-
ing. However, in order to make the decision to continue education, en-
trepreneurs may first rethink their past experiences with entrepreneur-
ship education.
Education Satisfaction, Quality, and Continuation
Overall satisfaction is defined as an emotional reaction to a product
or service experience (Spreng et al. 1996), while quality means meeting
the customer requirements (Oakland 1993). In the context of services of
management or entrepreneurship education participants may evaluate
the educational experience (1) in general terms, by their overall satisfac-
tion and in terms of their assessment on how good their overall require-
ments have been met, and (2) in specific terms, by evaluating different
elements of education quality such as the lecturer, the content, the pro-
cess, and the facility.
In spite of very different traditions and structures of adult and contin-
uing education in the world, it seems to be a common tendency that
the relation between adult education and work and the labor market
is becoming very important. All countries have very large adult educa-
tion needs, extending to all fields of adult education - general and voca-
tional, formal and non-formal. Although state and social partners' prior-
ity themes include education for the labor market and education related
to employment and vocational training, the state should include among
its priorities the study of system and policy and of educational needs,
and updating of adult education (Jelenc 1996,447).
Entrepreneurs acquire knowledge by engaging in formal education
and by taking part in courses related to different fields of interest, which
are important for managing the business. The success factors include
perseverance (building confidence and self-esteem), commitment to the
enterprise, and a positive attitude and approach. Developers of qualifica-
tions and training programs for post-secondary education and training
of entrepreneurs and potential entrepreneurs should (Labuschagne et al.
2001,17): (1) identify and integrate the outcomes from existing subjects
in the field of economic and management sciences that relate to the suc-
cess factors of entrepreneurs; and (2) supplement these outcomes with
case studies, experiential exercises and practical activities that will en-
able learners to integrate the knowledge and skills obtained in such train-
ing of entrepreneurs, and to apply these skills and knowledge in an en-
trepreneurial environment. The content-related techniques or method-
ologies that influence the success of entrepreneurship training are case
studies, simulations and business plan executions. Materials such as a
study guide, time schedules and facilitator guidelines that could improve
the evaluation are used, since they make more information available to
the evaluator. The evaluator must be knowledgeable about the field and
have experience in business and training entrepreneurship (Pretorius
2001, 14).
The majority of researches have explored the contents of education -
what entrepreneurs need in order to be successful - but training orga-
nizations want to know answers to questions about the quality of their
training programs as perceived by their clients. If the results of such eval-
uations are disappointing, the training organization can modify its pol-
icy and programs. The main method for collecting the data is a closed
questionnaire sent or distributed to the participants of training. Mulder
(2001, 323), for example, prepares a questionnaire that consists of eight
blocks of questions. Within the group of general questions one is about
the general satisfaction with the training project as a whole. Questions
about the objectives of the training project include some about the level
to which certain objectives are of importance within the project. These
objectives-related questions are aimed at: (1) attaining a learning result
(knowledge, skills, attitudes); (2) improving a changed work behavior in
the work situation; and (3) supporting the change of the organization;
(4) other factors are personal factors, training program factors, organi-
zational factors and transfer conditions.
Other scholars have also addressed different dimensions important for
the evaluation of education quality. In the study by Hill, Lomas, and
MacGregor (2003,16-18) four themes emerged in relation to what stu-
dents perceived quality education to be. In the order of importance to
the student group, these themes are: (1) quality of the lecturer (delivery
in the classroom, feedback to students during the session and in assign-
ments, relationship with students in the classroom); (2) student engage-
ment with learning (the students valued a curriculum that was related to
their worlds but broadened their horizons); (3) social/emotional support
systems (the students found support from college support systems, their
peers and families); and (4) resources of library and it. In the study by
Louw et al. (2001,44) the quality of mba program loaded on five factors:
(1) value of the mba program and the personal expectations of graduates
(six items); (2) coursework material (four items); (3) quality of lecturers
(three items); (4) learning methods (three items); and (5) interpersonal
and leadership skills (two items). In sum, education may be evaluated
in terms of satisfaction and perceived quality of the lecturer, education
content, environment, materials, methods, learning processes, and re-
sults (knowledge acquisition, skill training).
Loyalty means a positive evaluation as well as non-random continuous
purchases, usually of product brands (Mowen 1995). Oliva et al. (1992)
tried to explain why investments in a service fail. In their study they sup-
ported the predictions that the satisfaction-loyalty relationship could be
linear and non-linear, depending on the customer involvement. Despite
the possibility of non-linearity, in entrepreneurship education research
generally positive relationships between education satisfaction and con-
tinuation (loyalty) were found (Antoncic and Hvalic Erzetic 2001, 2003;
Antoncic et al. 2003). Besides the above mentioned findings from Slove-
nia, notable entrepreneurship research works were completed also in Ro-
mania, mostly on entrepreneurial education (Scarlat 2001, 2003; Scarlat
and Simion 2003).
In this paper we explore similarities and differences between Slove-
nia and Romania in entrepreneurship education satisfaction/quality and
loyalty, as well as in testing the hypothesis of the positive relationship
between education satisfaction/quality and education continuation.
Methods
In this section methodology (variables and measurement, sample and
data collection, and analysis) is presented.
variables and measurement
Independent variables are satisfaction and perceived quality of educa-
tion. First, satisfaction level was not assumed to be unidimensional as
proposed by Westbrook (1980), who used only one five-point Delighted-
Terrible Scale. Antoncic and Hvalic Erzetic (2001) discovered that mea-
suring satisfaction with entrepreneurship education on a single six-point
scale ranging from 'very satisfied' to 'very unsatisfied' results in a very
skewed answer distribution, which may not be usable for analysis with
continuous variables. Therefore, satisfaction was measured with eight
items on seven-point semantic differential scales answering a question
about the respondents' general feeling about the education they en-
gaged in (anchors: very dissatisfied-very satisfied, terrible-delighted,
very dissatisfied-not at all dissatisfied, not at all satisfied-very satisfied,
unfavorable-favorable, unpleasant-pleasant, I didn't like it at all-I like it
very much, frustrated-contented). Items were adapted from Crosby and
Stephens (1987), Eroglu and Machleit (1990), and Spreng et al. (1996).
Quality of education was measured with a 17-item scale that was de-
veloped as an extension of a 3-item service quality scale of Taylor and
Baker (1994). Measurement items were added by taking into consider-
ation different elements of the educational service such as overall as-
sessment of quality and fulfillment of expectations, education content,
evaluation of the lecturer, appropriateness of materials or handouts and
audiovisual aids, adequacy of room and equipment, and usefulness of
acquired knowledge.
Dependent variable - education continuation was measured as the re-
spondent's intention to continue his or her education in the future in
terms of his or her expressed loyalty to the educational program and
provider. Five questions were adapted from Bettencourt (1997), and Zei-
thaml et al. (1996): (1) saying positive things, (2) recommend to people,
who are thinking about education, (3) encourage friends and relatives
to engage in this education, (4) consider this education provider as a
first choice, and (5) engage more in education from this provider in the
next years. A seven-point Likert-type scale was used with anchors from
'strongly disagree' to 'strongly agree'.
Control variables data were collected about the respondent's educa-
tion type (degree, non-degree), age, gender, length of work experience,
education level, and industry of the company.
data collection and sample
Data were collected from Slovenian and Romanian practicing and poten-
tial entrepreneurs, as well as non-entrepreneurs, who engage in degree
and non-degree management education and training. A structured ques-
tionnaire was distributed mainly in class to conveniently selected groups
of participants.
Answers were received from 128 respondents from Slovenia and 135 re-
spondents from Romania. Our sample shows that 22.8% respondents in
Slovenia and 64.9% in Romania educate themselves through the post-
graduate program. In Slovenia 40.2% of respondents educate themselves
through the college/university program, in Romania 29.8%. Only 1.6%
of respondents in Slovenia and 1.5% in Romania educate themselves
through the secondary/high school program. At the non-degree educa-
tion type 17.3% in Slovenia and 21.4% in Romania usually participate in
workshops and seminars that last several weeks, 18.1% in Slovenia and
22.9% in Romania take one-day seminars. Percentages for Romania add
up to more than 100% since some participants are enrolled in more than
one type of education.
The age of respondents is mostly spread between twenty and fifty
years. 58.3% of respondents in Slovenia and 72.3% in Romania are more
than 20 and up to 30 years old, 22.0% in Slovenia and 23.8% in Romania
are more than 30 and up to 40 years old, and 12.6% in Slovenia and 3.1%
in Romania are more than 40 and up to 50 years old. In Slovenia 4.7% of
them are over 50 years old and 2.4% are up to 20 years old. In Romania
only 0.8% are up to 20 years old, no one is over 50 years old.
In Slovenia, 56.8% of respondents are female and 43.2% are male. In
Romania 45.4% are female and 54.6% are male. 53.5% of respondents
in Slovenia and 71.5% in Romania are single. 40.2% of respondents in
Slovenia and 27.7% in Romania are married, of whom 6.3% and 0.8% are
divorced or widowed. Respondents have various professions but most of
them are economists.
The education level of the sample is as follows: in Slovenia 50.8% of
respondents have secondary or high school diploma, 31.7% have college
or university degree, 15.1% have vocational school. In Romania 70.8% of
respondents have college or university degree, 26.9% of respondents have
post-graduate degree, and 1.5% of them have secondary or high school
diploma.
The length of work experience is spread from zero to thirty years,
but most of the respondents have had work experience of five years or
less: Slovenia 50.4%, Romania 62.5%; more than five and up to ten years:
Slovenia 12.4%, Romania 23.4%; more than ten and up to twenty years:
Slovenia 21.5%, Romania 11.7%, and more than twenty and up to thirty
years: Slovenia 12.4% and Romania 2.3% of respondents.
The sample consists of 16.9% practicing entrepreneurs in Slovenia and
26.3% in Romania. 18.6% of respondents in Slovenia and 36.1% in Ro-
mania will start-up their own business, 48.4% in Slovenia and 33.6% in
Romania will maybe establish their own business. 15.4% of respondents
in Slovenia and 4.1% in Romania do not intend to establish their own
business. Most of practicing entrepreneurs in Slovenia (10.5% of respon-
dents) have had their own business more than five and up to ten years (in
Romania 3.3%), most of practicing entrepreneurs in Romania (7.4% of
respondents) have had their own business more than two and up to five
years (in Slovenia 0.0%), and the same percentage in Romania (7.4%)
have had their own business less than one year (in Slovenia 3.2%). Most
of the potential (prospective) entrepreneurs (in Slovenia 6.5% and in Ro-
mania 9.8% of respondents) will establish their own business in less than
one year, 3.2% in Slovenia and 6.6% in Romania will establish their own
business in one year, 8.9% in Slovenia and 19.7% in Romania in two to
three years.
The respondents come from different industries, most of them (13.7%
in Slovenia and 12.2% in Romania) operate in consulting and business
services; 27.8% of respondents in Romania and 11.8% in Slovenia come
from customer services. As seen from this comparison of the two sam-
ples, the samples can be comparable to some degree but they do not
match perfectly.
analysis
The means of all items were compared between the two countries by as-
sessing their values and performing t-tests. Multi-item scales of satisfac-
tion, quality and education continuation (loyalty) were checked for their
convergent validity by using exploratory factor analysis and the Cron-
bach Alpha reliability measure. All Cronbach Alphas were very high indi-
cating very good reliablitiy: for satisfaction (8 items) - Slovenia 0.96, Ro-
mania 0.95, for continuation (loyalty) (5 items) - Slovenia 0.92, Roma-
nia 0.94, and for quality dimensions: content/process (6 items) - Slove-
nia 0.89, Romania 0.94, knowledge acquisition (3 items) - Slovenia 0.83,
Romania 0.80, and room/equipment adequacy (2 items) - correlations:
Slovenia 0.78, Romania 0.80. The education quality construct was as-
sessed for dimensionality by using exploratory factor analysis, resulting
in three distinct quality dimensions: (1) quality of education content and
process, (2) quality and usefulness of acquired knowledge, and (3) quality
and adequacy of room and equipment. For satisfaction, the three qual-
ity dimensions, and education continuation construct a single item that
was computed as the mean of all items. This was done in order to re-
duce the number of variables for subsequent analysis. The key hypothesis
was tested by using regression analyses with two country-based groups
of data.
Findings
education satisfaction, quality, and loyalty levels
Mean values for all education satisfaction, quality, and loyalty items were
compared. Most of the item means were found not to be different be-
tween Slovenia and Romania. T-test statistical differences (at a 0.05 level)
were discovered only for three quality items. The item 'knowledge, which
is acquired, will be very useful in my work' was rated higher in Slovenia
(4.8) than in Romania (4.2). The mean of the item 'The equipment in the
room, in which education was performed, is totally adequate' was found
higher in Slovenia (5.0) than in Romania (4.6). The item 'The education
table 1 Regression analysis results
(1) (2) (3) (4)
(5) (6)
(7)
Slovenia (constant)
Satisfaction
Quality
•	content and process
•	acquired knowledge
•	room and equipment
0.219	0.434	0.504	0.615
0.223	0.095	0.202	2.350	0.020	0.457	2.190
0.795	0.113	0.648	7.042	0.000 0.400 2.500
-0.027	0.069	-0.026	-0.394 0.694 0.761	1.314
-0.053	0.053	-0.062 -1.009	0.315	0.900 1.111
Romania (constant)
Satisfaction
Quality
•	content and process
•	acquired knowledge
•	room and equipment
-0.504	0.389
0.386	0.088	0.297
0.418	0.106	0.379
0.096	0.068	0.095
0.173	0.065	0.206
-1.294	0.198
4.408	0.000	0.602	1.660
3.956	0.000	0.298	3.351
1.416	0.159	0.612	1.634
2.674	0.009	0.462	2.165
Note: Column headings are as follows: (1) b; (2) standard error [1-2 unstandardized
coefficients]; (3) beta [standardizedcoefficient]; (4) t; (5) sig.; (6) tolerance; (7) vif
[6-7 collinearity statistics]. Dependent variable: loyalty.
content was adequate' also received a higher rate in Slovenia (5.3) than in
Romania (5.0).
When means were compared between the two country groups at the
construct and dimension level no differences were found, except one -
the acquired knowledge quality dimension was found higher in Slovenia
(5.0) than in Romania (4.7).
the education continuation predictability
Results of the multiple regression analysis, which tested relationships
between education continuation (loyalty) and its predictors (educa-
tion satisfaction, and three quality dimensions), are shown in table 1.
Adjusted R-squares of the multiple regression models were found high
in both countries (Slovenia 0.60, Romania 0.65) indicating that almost
two thirds of variance in loyalty can be explained by satisfaction, con-
tent/process quality, acquired knowledge quality, and room and equip-
ment adequacy. This is in support of the overall hypothesis on the rela-
tionship between satisfaction/quality and loyalty.
However, in the Slovenian sample coefficients of only half of regres-
sion elements were found significant (satisfaction: standardized coef-
ficient 0.20, and content/process quality: st. coef. 0.65). In the Roma-
nian sample coefficients of satisfaction (st. coef. 0.30), content/process
quality (0.38), and room/equipment adequacy (0.21) were significant,
whereas the acquired knowledge quality coefficient was low (0.07) and
non-significant. These results are also in some support of the overall hy-
pothesis on the satisfaction/quality-loyalty relationship, with a difference
between the two countries in the impact of room/equipment quality on
loyalty, which was found in Romania, but not in Slovenia. In both coun-
tries, content/process quality, as the most important, and satisfaction
were found the strongest predictors of education continuation (loyalty).
When unstandardized coefficients were compared between the two
samples, we discovered that relative impacts of three predictors differ
between the two countries (more than two standard error difference).
Content and process quality seems to have a stronger impact on loyalty in
Slovenia (unstandardized coefficient 0.80) than in Romania (unst. coef.
0.42), whereas the impact of satisfaction and room/equipment quality
can be considered stronger in Romania than in Slovenia (unst. coef.:
satisfaction - Romania 0.39, Slovenia 0.22; room/equipment - Romania
0.17, Slovenia -0.05).
other findings
Other findings based on our data are summarized as follows (see also
table 2):
•	Underlying reasons for engagement in education. The first reason,
why entrepreneurs decide for education, is that they want to ob-
tain a higher degree: 54.7% in Slovenia, whereas 50.8% of the Ro-
manian sample want to gain knowledge in a new area. The acquisi-
tion of knowledge in a new area is the second reason for Slovenian
respondents (46.1%); the second reason for entrepreneurs in Roma-
nia (41.7%) is that they want to learn how to do business in a fast
growing firm. This is the third reason in Slovenia (23.4%). The third
reason in Romania is that entrepreneurs want to obtain a higher
degree (40.2%). This difference may be somewhat sample specific
- lower education levels of the Slovenian sample in comparison to
the Romanian one.
•	Main areas of education. Slovenian sample: 57.8% of entrepreneurs
educate themselves in the entrepreneurship area of education, 19.5%
in marketing and sales and 18.8% in finance; in Romania: 32.6% in
computing, 25.8% in marketing and sales, and 19.7% in manage-
ment.
table 2 Other results
	Slovenia	Romania
Education satisfaction		
very satisfied	10.9%	10.1%
satisfied	67.2%	67.4%
a little satisfied	18%	15.5%
a little unsatisfied	3.1%	3.9%
unsatisfied	0.8%	3.1%
Education continuation		
yes	59.4%	61.4%
probably yes	33.6%	25.8%
maybe	7.0%	10.6%
probably no	0.0%	1.5%
no	0.0%	0.8%
Education type		
degree	68.5%	96.2%
non-degree	35.4%	44.3%
Reasons for education		
get higher degree	54.7%	40.2%
renew knowledge	6.3%	28.0%
get knowledge in new area	46.1%	50.8%
learn about fast growing firm	23.4%	41.7%
be informed about news in the world	7.0%	22.0%
Reasons for education continuation		
get higher degree	44.6%	44.3%
renew knowledge	22.3%	36.1%
get knowledge in new area	55.4%	49.2%
learn about fast growing firm	30.6%	41.8%
be informed about news in the world	35-5%	23.8%
Continued on the next page
•	Opinion about the necessity of education. 61.9% of respondents in
Slovenia and 56.5% of respondents in Romania think that en-
trepreneurs must educate themselves more than once a year, whe-
reas 38.1% of respondents in Slovenia and 40.5% of respondents in
Romania think that they must educate themselves at least once a
year.
•	Overall satisfaction with education. 10.9% of respondents in Slove-
nia and 10.1% in Romania are very satisfied with education, 67.2%
of respondents in Slovenia and 67.4% in Romania are satisfied with
education, 18.0% in Slovenia and 15.5% in Romania are a little sat-
isfied with education, 3.1% in Slovenia and 3.9% in Romania are a
table 2 (continued)
	Slovenia	Romania
Area of education		
marketing and sales	19.5%	25.8%
finance	18.8%	13.6%
entrepreneurship	57.8%	2.3%
management	5.5%	19.7%
computing	11.7%	32.6%
Area of education -future		
marketing and sales	33.6%	38.6%
finance	31.9%	21.3%
entrepreneurship	43.7%	13.4%
management	19.3%	29.1%
computing	26.1%	26.8%
Education - necessity		
more than once a year	61.9%	56.5%
at least once a year	38.1%	40.5%
not necessary	0.0%	3.1%
little unsatisfied, and only 0.8% of respondents in Slovenia are un-
satisfied with education whereas in Romania this percentage is 3.1%.
•	Overall education continuation. 59.4% of entrepreneurs in Slovenia
and 61.4% of respondents in Romania say that they will definitely
continue with education, 33.6% in Slovenia and 25.8% in Romania
say that they will probably continue with education and 7.0% of
them in Slovenia and 10.6% in Romania will maybe continue with
education. In Romania 1.5% say that they will probably not con-
tinue with education.
•	Reasons for education continuation. The first reason for education
continuation is getting knowledge in a new area (55.4% of en-
trepreneurs in Slovenia and 49.2% in Romania think so). 44.6%
of respondents in Slovenia and 44.3% in Romania will continue
with education to get a higher degree, 35.5% in Slovenia will con-
tinue because they wish to be informed about news in the world and
41.8% of respondents in Romania want to learn how to do business
in a fast growing firm.
•	Main areas of future education. Slovenia: 43.7% of entrepreneurs will
educate themselves in an entrepreneurship area, 33.6% in marketing
and sales, and 31.9% in finance. Romania: 38.6% of entrepreneurs
will educate themselves in marketing and sales, 36.2% in an en-
trepreneurship area, and 29.1% in management.
Discussion and Conclusion
In this paper we provided evidence for the existence of positive relation-
ships between education satisfaction elements and education continu-
ation by conducting a cross-cultural study in two countries (Romania
and Slovenia). We found that education content and process quality,
and education satisfaction of participants tend to be the strongest pre-
dictors of the subsequent decision to continue education in both coun-
tries. Room and equipment adequacy may also be important, but our
findings showed this result only for Romania. Surprisingly, we did not
find support for the impact of acquired knowledge quality on education
continuation. Since the calculated correlations between acquired knowl-
edge quality and continuation were significant and not very low, both in
Slovenia (0.34) and in Romania (0.55), the lower regression coefficients
than expected may be due to the fact that satisfaction and quality dimen-
sions were correlated, even though we did not encounter bigger problems
of multicollinearity in regression analyses.
An interesting finding of our research is that content and process
quality tends to have a stronger impact on education continuation
in Slovenia than in Romania, whereas the impact of satisfaction and
room/equipment quality may play in Romania a stronger role in the de-
cision to continue education than in Slovenia. This finding leads to an
important conclusion for education providers in management and en-
trepreneurship education. In order to maximize the retention of partic-
ipants and probably improve also business results, education providers
in Slovenia need to pay good attention to content/process issues such
as excellence, fulfillment of expectations of participants, well prepared
materials, and the selection of the lecturer, who needs to satisfy partic-
ipants in general, as well as convey the subject matter in an interesting
and clear way. Romanian education providers may like to consider tak-
ing a somewhat different approach, that is, they need to try to satisfy
the participants in general, as well as to provide an adequate education
environment in terms of the room in which education is performed and
equipment that is used.
In this study we also provide some evidence on the cross-cultural va-
lidity and comparability of the measures of education satisfaction, qual-
ity, and continuation, while pointing out some differences between man-
agement and education participants in the two countries. In Romania
the key reason for participants to engage in education is a will to acquire
new knowledge, while in Slovenia it tends to be a decision to obtain a
higher degree. In the decision to continue education, participants from
both countries give the leading role to new knowledge acquisition, and
also consider continuing education a necessity, which we generally con-
sider a positive sign for both countries. An interesting difference emerged
in the consideration of a future area of education. In Slovenia, the en-
trepreneurship area may be the most prominent (followed by marketing
and sales, and finance), whereas in Romania marketing and sales may be
the leading area, followed by computing and general management.
The study has some limitations. The samples used were not random
and not ideally matched across the two countries. Measures were based
on perceptions and intentions and not on actual behavior of partici-
pants, which would require a longitudinal study design.
The study was conducted in management and entrepreneurship edu-
cation in two countries; future research may further validate the results
of this study in other countries and contexts. Despite the limitations, we
provided some interesting conclusions, which are important in under-
standing the relationship between education satisfaction and quality as
predictors of education continuation, as well as in managing education
contents and processes.
References
Antoncic, B., and B. Hvalic Erzetic. 2001. Entrepreneurial education con-
tinuation: Role of maturity and satisfaction. In Conference 2001: In-
ternationalising entrepreneurship education and training, 2-4th July,
Kruger park, South Africa, 263-80.
Antoncic, B., and B. Hvalic Erzetic. 2003. Development and empirical test-
ing of a measure of entrepreneurship education satisfaction and qual-
ity. In IntEnt2003: Internationalizing entrepreneurship education and
training; 13th annual conference, 7-10 September. Grenoble: Institut
National Polytechnique.
Antoncic, B., B. Hvalic Erzetic, and O. Zorn. 2003. Management education
of entrepreneurs: Non-linearity in the satisfaction-continuation rela-
tionship. In Conference proceedings, ed. D. H. Nagao. Seattle, August
1-6. Seattle, wa : Georgia Institute of Technology.
Bettencourt, L. A. 1997. Customer voluntary performance: Customers as
partners in service delivery. Journal of Retailing 73 (3): 388-406.
Cho, B. 1998. Study of the effective entrepreneurship education method
and its process. Business Education Research 2 (1): 27-47.
Crosby, L. A., and N. Stephens. 1987. Effects of relationship marketing on
satisfaction, retention, and prices in the life insurance industry. Jour-
nal of Marketing Research 24 (November): 404-11.
Eroglu, S. A., and K. A. Machleit. 1990. An empirical study of retail crowd-
ing: Antecedents and consequences. Journal of Retailing 66 (Summer):
201-21.
Ehrlich, S. B., A. F. De Noble, D. I. Jung, and D. Pearson. 2000. The im-
pact of entrepreneurship training programs on an individual's en-
trepreneurial self-efficacy. In Frontiers of Entrepreneurship Research,
ed. P. D. Reynolds, W. D. Bygrave, S. Manigart, C. Mason, G. D. Meyer,
H. Sapienza, and K. Shaver. Wellesley, ma: Babson College.
Han, J., and M. Lee. 1998. A study on the status of entrepreneurship ed-
ucation in Korea and its direction. Business Education Research 2 (2):
5-26.
Henry, C., F. Hill, and C. Leitch. 2003. Entrepreneurship education and
training. Aldershot: Ashgate.
Hill, Y., L. Lomas, and J. MacGregor. 2003. Students' perceptions of quality
in higher education. Quality Assurance in Education 11 (1): 15-20.
Jelenc, Z. 1996. Adult education research in the countries in transition: Adult
education research trends in the former socialist countries of Central
and Eastern Europe and the Baltic region. Ljubljana: Andragoški cen-
ter Slovenije.
Labuschagne, M., C. Nieuwenhuizen, and J. Kroon. 2001. A multi-discipli-
nary approach to the development of training programmes and qual-
ifications for entrepreneurship. Paper presented at the IntEnt 2001
Conference, Kruger park, South Africa, July 2-4.
Lee, S. M., D. Chang, and S. Lim. 2005. Impact of entrepreneurship ed-
ucation: A comparative study of the us and Korea. International En-
trepreneurship and Management Journal 1 (1): 27-43.
Louw, L., J. K. Bosch, and D. J. L. Venter. 2001. Graduates' perceptions of
the quality of mba programmes. Quality Assurance in Education 9 (1):
40-5.
Mowen, J. C. 1995. Consumer behavior. Englewood Cliffs, nj: Prentice-
Hall.
Mulder, M. 2001. Customer satisfaction with training programs. Journal of
European Industrial Training 25 (6): 321-31.
Oakland, J. S. 1993. Total quality management: The route to improving per-
formance. Oxford: Butterworth-Heineman.
Oliva, T. A., R. L. Oliver, and I. MacMillan. 1992. A catastrophe model
for developing service satisfaction strategies. Journal of Marketing 56
(July): 83-95.
Postigo, S., D. Iacobucci, and M. F. Tamborini. 2003. Undergraduate stu-
dents as a source of potential entrepreneurs: A comparative study
between Italy and Argentina. In IntEnt2003: Internationalizing en-
trepreneurship education and training; 13th annual conference, 7-10
September. Grenoble: Institut National Polytechnique.
Pretorius, M. 2001. Application of a proposed evaluation instrument to
evaluate venture start-up training programmes. Paper presented at the
IntEnt 2001 Conference, Kruger park, South Africa, July 2-4.
Rushing, F. W. 1990. Entrepreneurship and education. In Entrepreneur-
ship education: Current developments, future directions, ed. C. A. Kent.
Westport, ct: Quorum Books.
Scarlat, C. 2001. New spirit of enterprise in Romania. In Introduction to
entrepreneurship, 83-93. Vol. 5 of Research in business management.
Bucharest: Printech.
Scarlat, C. 2003. Antreprenoriat si managementul intreprinderilor mici si
mijlocii. Bucharest: Printech.
Scarlat, C., and C. Simion. 2003. Entrepreneurial education in University
'Politehnika' of Bucharest. In uNiversite dans la societe- uniso 2003,
eds. S. E. Zaharia and V. Muresan, 233-9. Bucharest: Paideia.
Spreng, R. A., S. B. MacKenzie, and R. W. Olshavsky. 1996. A reexamina-
tion of the determinants of consumer satisfaction. Journal of Market-
ing 60 (July): 15-32.
Taylor, S. A., and T. L. Baker. 1994. An assessment of the relationship be-
tween service quality and customer satisfaction in the formation of
consumers' purchase intentions. Journal of Retailing 70 (2): 163-78.
Westbrook, R. A. 1980. A rating scale for measuring product/service satis-
faction. Journal of Marketing 44 (Fall): 68-72.
Young, J. E. 1997. Entrepreneurship education and learning for university
students and practicing entrepreneurs. In Entrepreneurship 2000, ed.
D. L. Sexton and R. W. Smilnor, 215-38. Chicago, il: Upstart Publish-
ing Company.
Zeithaml, V. A., L. L. Berry, and A. Parasuraman. 1996. The behavioral
consequences of service quality. Journal of Marketing 60 (April): 31-46.
Managing Global Transitions
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Beech, M. H. 1982. The domestic realm in
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Separate worlds: Studies of purdah in
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Jackson, R. 1979. Running down the
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volume 3 • number 2 • fall 2005 ' ISSN I581-63II
117 Corporate Ownership, Leadership and Job
Charateristics in Russian Enterprises
Moshe Banai
Jacob Weisberg
139 Joint Dynamics of Prices and Trading Volume
on the Polish Stock Market
Henryk Gurgul
Pawel Majdosz
Roland Mestel
157 Human Capital and Economic Growth
by Municipalities in Slovenia
Matjaž Novak
Štefan Bojnec
179 Where is the Border Between an Information System
and a Knowledge Management System?
Imandra Galandere-Zile
Viktorija Vinogradova
197 The Quality of Entrepreneurship Education
and the Intention to Continue Education:
Slovenia and Romania
Boštjan Antončič
Cezar Scarlat
Barbara Hvalic Erzetič
FAKULTETA ZA | FAGOLTÄ DI | FACULTY OF
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