5
MODEL CHARACTERISTICS OF OLYMPIC
SPORT ORGANISATIONS IN SLOVENIA
MODELNE ZNA^ILNOSTI OLIMPIJSKIH
[PORTNIH ORGANIZACIJ V SLOVENIJI
Jakob Bednarik*
Franci Ambro`i~*
Renata Mo~nik*
Marko Kolenc**
Marjeta Kova~**
Bednarik, J., Ambro`i~, F., Mo~nik, R., Kolenc, M., & Kova~, M. (2000). Model Characteristics of… KinSI 6(1–2), 5–11
Abstract
The study deals with the characteristics of Olympic-
sports organisations in Slovenia and the main differ-
ences in relation to non-Olympic sports organisations
dealing either with competitive or recreational sport.
Two samples were obtained. The data on the em-
ployee structure comes from a sample of 366 sports
organisations, representing about one sixth of all
sports organisations, while the financial data mirrors
incomes and expenditures of fifty-five sports. The or-
ganisations and sports were classified into Olympic,
non-Olympic competitive sports and recreational
sports ones. The cadre and financial structure and the
differences were analysed with descriptive statistics,
chi-square statistics and canonical discriminant anal-
ysis.
Slovenian sports organisations are mainly managed
voluntarily. Olympic sports organisations are situated
mostly in cities, are smaller, have much higher in-
comes and expenditures and a relatively high level of
professionalisation. Statistically significant differences
were found between the three types of organisations
in size, seat, income and expenditure and cadre struc-
ture. The most discriminating variables were the num-
ber of voluntary technical staff, honorary administra-
tive staff, voluntary medical staff and full-time
instructors.
Keywords: sport, organisation, Olympic sport, non-
Olympic sport, recreational sport, model, Slovenia
Izvle~ek
V ~lanku so analizirane modelne zna~ilnosti tistih
{portnih organizacij, katerih {portna panoga je na pro-
gramu olimpijskih iger. Ugotovljene so tudi glavne ra-
zlike med temi {portnimi organizacijami in tistimi, ki
se ukvarjajo s tekmovalnim {portom v ne-olimpijskih
panogah ali rekreativnim {portom.
Dobljena sta bila dva vzorca. Podatki o strukturi za-
poslenih izhajajo iz vzorca 366 {portnih organizacij,
medtem ko finan~ni podatki ka`ejo prihodke in
odhodke petinpetdesetih {portnih panog. Organiza-
cije in {portne panoge so bile razvr{~ene v tri skupine:
olimpijske, ne-olimpijske tekmovalne in rekreativne.
Kadrovska in finan~na struktura ter razlike med
skupinami so bile analizirane z opisno statistiko, χ
2
testom in kanoni~no diskriminantno analizo.
Olimpijske {portne organizacije se nahajajo v glavnem
v mestih, so manj{e, imajo veliko vi{je prihodke in
odhodke ter relativno visoko stopnjo profesionalno
zaposlenih delavcev. Statisti~no zna~ilne razlike so
bile dobljene med tremi skupinami organizacij v ve-
likosti, sede`u, prihodkih in odhodkih ter strukturi
kadrov. Najbolj diskriminativne spremenljivke so bile
{tevilo volonterskih tehni~nih delavcev, honorarnih
administrativnih delavcev, medicinskega osebja in
redno zaposlenih u~iteljev.
Klju~ne besede: {port, organizacija, olimpijski {port,
ne-olimpijski {port, rekreativni {port, model, Slo-
venija
(Received: 4. 12. 2000 – Accepted: 12. 12. 2000)
*University of Ljubljana, Faculty of Sport, Ljubljana, Slovenia
**Slovenian Sports Office, Ljubljana, Slovenia
Contact address
Jakob BEDNARIK
Univerza v Ljubljani – Fakulteta za {port, Gortanova 22, SI-1000
Ljubljana, Slovenia
Tel: +386 1 540-10-77
Fax: +386 1 540-22-33
E-mail: jakob.bednarik@sp.uni-lj.si

6
INTRODUCTION
Every human activity has a function, a purpose. The
human activity we call sport has, like all other human
activities, different functions and purposes: winning a
competition, education, relaxation, health preserva-
tion, rehabilitation, earning, diversion for viewers and
above all a way of life, which contains the substance
we call ”the quality of life” (Bednarik and Petrovi},
1998). Therefore the question arises which are the
most significant.
When we speak about Olympic sport and mean the
sportsmen that will participate at the Olympic games,
or are trying to qualify, we have automatically also de-
fined the most important function. Namely,
Coubertin’s saying ”the important thing in the
Olympic Games is not winning but taking part”
(Schoedel, 1968) has not held for a long time; now,
the maxim is ”winning (achieving the best possible re-
sult) is everything”. Only such results trigger then the
satisfaction and interest of the spectators. According
to this maxim, the sports pleasure of the public then
verifies and justifies the efforts of all involved
(Bednarik, Petrovi} and Nyerges, 1997). Watching a
sports event springs from the inner need of the spec-
tator who nurtured this need through his interest for
sport. Only in this way does the top-level sports result
become a multiplicator, a generator of mass sports
and sport industry, their promoter and the promoter
of the native country of the athlete and coach and al-
so of the sports event in which the result was
achieved. It is precisely these functions of the sports
result that define the financial part of its exchange val-
ue (Bednarik, Petrovi} and Nyerges, 1997).
Slovene athletes have won 185 medals in Olympic
sports and 98 medals in non-Olympic sports between
1996 and 1999 at the highest level competitions (se-
nior World Championship, senior European Cham-
pionship, World Student Games, junior World Cham-
pionship, junior European Championship). At the last
summer Olympics two Slovene athletes won two
medals. Relative to the number of inhabitants (1.98
million), Slovene sport ranks among the most suc-
cessful in the world (Bednarik, Petrovi} and Nyerges,
1997; Bednarik and Petrovi}, 1998). Slovene sport is
organised in clubs. These can be placed into the third
sector according to the typology of Chelladurai
(1985), which is financed from public and private
funds, but established and managed by the private
sector. The sports organisations in Slovenia had in
1997 at their disposal 344 million EUR (Bednarik,
Petrovi} and [ugman, 1998). In 1997, 2130 clubs
(Kolenc and Bednarik, 1999) reported 90,000 ath-
letes and 265,000 members (Bednarik, Kolenc,
Petrovi}, Simoneti and [ugman, 1998). 
From the works of various authors (Amis and Slack,
1996; Verhoeven et al., 1997; Kikulis, Slack and
Hinings, 1995a; Theodoraki and Henry, 1994) we can
suppose that predominantly professional organisations
differ from predominantly voluntary ones in the num-
ber of members, amount of funds, cadre structure,
competitive or recreational activity, etc. Even if
Hirschmann (1974) found that clubs and their asso-
ciations are predominantly voluntary organisations,
one can ask if this also holds for sports organisations
dealing mostly with top level sport – Olympic or non-
Olympic sports – or just for organisations mostly deal-
ing with recreational sport. The main purpose of this
study is just that: finding some model characteristics
of sports organisations, not just in light of their volun-
tary or professional organisation, but comparing those
dealing mostly with top level sports in Olympic and
non-Olympic sports, and those dealing with recre-
ational sports.
Slovene sport has products, which are comparable to
global ones and also successfully satisfies its functions
(Bednarik and Petrovi}, 1998). Cognisance of the
sport organisations’ model characteristics gives the
possibility of re-organisation and new organisation in
a way that we know gives good results. Olympic sports
have some specific demands due to their competi-
tion cycles in comparison with non-Olympic sports
and are mostly more commercially interesting
(Bednarik, Simoneti, Petrovi} and [trumbelj, 1998),
we can therefore suppose that their model charac-
teristics differ. Since sports organisations, which are
not involved in sports competition have a different
product from those that are (Chelladurai, 1985), it
seems rational to expect different organisational char-
acteristics, which should be taken into account in
managing sport.
METHODOLOGY
Subject sample
Two samples were analysed. The first consisted of 55
sports (financial data) and the second of 366 sports or-
ganisations (cadre structure).
The financial data (incomes and expenditures) for the
individual organisations was unfortunately not avail-
able to us, so data summed into sports – the data of
all organisations of the same sport are given as a sum
– was used instead. Fifty-five sports (sums of data on
individual organisations) were analysed. The sports
were classified into two groups – Olympic and non-
Olympic sports.
The population of sports organisations (henceforth or-
ganisations) was defined as all the national sports as-
sociations, communal sports associations and sport
clubs entered into the register of sports organisations
Bednarik, J., Ambro`i~, F., Mo~nik, R., Kolenc, M., & Kova~, M. (2000). Model Characteristics of… KinSI 6(1–2), 5–11

7
in Slovenia, compiled after passing the new Law on
Clubs (Zakon o dru{tvih, 1995). There were 2,225
such units registered at the time of observation, 464
organisations returned our questionnaire, but after
eliminating some which did not complete the ques-
tionnaire, the sample analysed here consists of 366
units (organisations and clubs) which gave at least all
the basic data (16.5 % of the total population). The or-
ganisations were classified into three groups – Olym-
pic, non-Olympic competitive and non-Olympic re-
creational.
Variable sample
The complete questionnaire for sports organisations
consisted of more than sixty items; we shall present
here only those analysed for this study.
SIZE – size of the unit (1=-100, 2=101-250,
3=251-500, 4=500- members)
SEAT– located in: 1=rural, 2=urbanfringe, 3=city
The next thirty variables recorded the number of em-
ployees, according to the nature of association with
the unit (voluntary, employed part-time, full-time em-
ployees) and their function (training: coaches of all
levels (code numbers s1, s2, s3), umpires (s4) and
medical staff (s5); managing: managers (f1), adminis-
trative personnel (f2), competition organisers (f3),
technical staff (ft)). The variable code is in two parts: 
(1) voluntary work (ZV), retained part-time (ZH) or
professional full-time (ZP)
(2) denotes their function, ex.: s4=referee (see codes
in parenthesis above).
The criterion variable (OLYMPIC), defining the nature
of the unit, was derived from the sport(s) practised in
the unit and the nature of the activity (competitive,
recreational). It divides the sports organisations into
three groups – Olympic, non-Olympic and recre-
ational sport.
In the case of the sports sample, the following vari-
ables were analysed: total income, sale income, total
expenditure and labour costs. The sale income and
labour costs were also computed as relative values
(percentages).
Data analysis
The data was analysed in three stages. First the de-
scriptive statistics of the variables – frequencies and
percents for nominal and ordinal variables and the
basic central tendency and dispersion parameters for
the scale data – were computed.
In the second stage the predominant nature of each
organisation was determined (Olympic, non-Olym-
pic, recreational), according to the sport(s) practised
in the organisation and level of competition or non-
competition.
In the third stage contingency tables were construct-
ed and the chi-square statistic computed to test the
difference between the organisation types in the in-
dividual independent variables. Canonical Discrimi-
nant Analysis was also used to see which of the kinds
of employees or financial data best differentiated be-
tween the organisation types. All differences (Chi-
square in contingency tables or Wilks’ Lambda in
canonical discriminant analysis) with an error less than
5 % were judged as statistically significant.
RESULTS
The majority of sport organisations in Slovenia have
up to 100 members, one fifth up to 250 members;
larger organisations are rather scarce (Table 1). It is in-
teresting to note that ”Olympic sport organisations”
are the most numerous, followed by ”recreational-
sport organisations” and ”competitive non-Olympic
sports”. The three types of organisations differ in size,
which is attested by the statistically significant χ
2
(Table 2). Olympic organisations are smaller than the
other two, most of the largest organisations are non-
Olympic competitive ones.
Almost half of the organisations have their base in
cities, the rest are almost evenly divided between
those in the suburbs and rural areas. The differences
between the three types are statistically significant
(Table 3). The difference between Olympic and non-
Olympic competitive sport organisations are small,
with more Olympic-sport organisations based in cities.
However, recreational-sport organisations differ a lot
Bednarik, J., Ambro`i~, F., Mo~nik, R., Kolenc, M., & Kova~, M. (2000). Model Characteristics of… KinSI 6(1–2), 5–11
Variable/Code 1234 M i ssi n g
SIZE 61.2 21.9 7.4 9.0 0.5
SEAT 25.7 23.2 48.6 2.5
OLYMPIC 42.1 26.2 31.7
Table 2: Differences between organisations – size
Size/Sport Olympic non-Olympic Recreational
– 100 56.5% 60.0% 69.6%
101 – 250 31.8% 16.8% 13.0%
251 – 500 6.5% 6.3% 9.6%
501 - 5.2% 16.8% 7.8%
Table 1: Distributions of nominal and ordinal vari-
ables
Remark: Only percentage values are given due to lack of space, for
value codes see the description of variables section.
χ
2
= 24.1 C = .249 p(χ
2
) = .000

8
from both, being almost equally based in urban and
rural milieus.
The general cadre structure of Slovene sport organi-
sations will not be presented due to limited space.
However, some points are worth noting. If we first
look at the number of staff (voluntary, professional and
all), great differences can be seen between the indi-
vidual organisations, with numbers ranging from 1 to
over 500. This great variability can be seen also from
the standard deviations, but inspection of the means
shows that a typical organisation has between fifteen
and twenty cadres, larger ones are the exceptions. It
might also be worth mentioning that the volunteers
outnumber professionals four to one. Inspection of
the cadre structure according to their function shows
that the most numerous are coaches (of all levels), or-
ganisers of competitions and umpires. This holds true
for all types 
–
voluntary, retained part-time or full-time
employees. Technical staff (ft) are present in all the
three categories, while medical staff (s5) and man-
agers (f1) are mostly voluntary. 
The multivariate differences between the groups
(Olympic/non-Olympic/recreational) in cadre struc-
ture and financial indicators given in Table 4. Three
comparisons have been made – the first between
Olympic and non-Olympic sport organisations in
cadre structure, the second among all three types in
cadre structure and the third between Olympic and
non-Olympic sports in financial indicators. The data
for the discriminative functions are given at the head
of the Tables. Both analyses of the cadre structure gave
statistically significant differences, while the multi-
variate differences in financial parameters were not
statistically significant.
An inspection of the discriminant correlation coeffi-
cients shows that the most discriminative variables for
the first comparison (Olympic/non-Olympic) are: the
number of voluntary technical staff, part-time admin-
istrative staff, voluntary medical staff and part-time in-
structors. The solution for three-way comparison
(Olympic/non-Olympic/recreational) is very similar,
with a slightly changed order of importance. The most
important discriminators are: the number of part-time
coaches, voluntary medical staff, voluntary managers,
part-time administrative staff, voluntary umpires, vol-
untary technical staff and voluntary coaches. The post-
hoc univariate difference testing shows that these in-
dicators are statistically significant univariate discrim-
inators as well. Table 5 shows typical cadre models
for the three types of organisations. In general,
Olympic-sport organisations have more employees of
all types than non-Olympic-sport organisations, and
both more than recreational-sport organisations. In
some indicators there are already differences between
the first two types, while in others they differ only
from recreational-sport organisations.
Bednarik, J., Ambro`i~, F., Mo~nik, R., Kolenc, M., & Kova~, M. (2000). Model Characteristics of… KinSI 6(1–2), 5–11
Table 3: Differences between organisations – seat
Seat/Sport Olympic non-Olympic Recreational
rural 16.8% 22.3% 42.1%
urban fringe 25.5% 26.6% 19.3%
city 57.7% 51.1% 38.6%
χ
2
= 22.7 C = .245 p(χ
2
)=.000
Table 4: Discrimination between Olympic, non-
Olympic & recreational organisations
Function Eigenvalue % variance can. corr. Wilk’s λ sig.
(1) 1 .222 100.0 .426 .819 .010
(2) 1 .242 74.8 .441 .745 .000
2 .081 25.2 .274 .925 .388
(3) 1 .122 100.0 .330 .891 .217
Variable DCC(1) F(1) Sig. F(1) DCC(2) F(2) Sig. F(2)
ZVS1 .009 .01 .944 .005 .00 .998
ZVS2 .058 .19 .665 .045 .13 .875
ZVS3 .002 .00 .988 .261 5.09 .007
ZVS4 .174 1.66 .199 .302 4.07 .018
ZVS5 .319 5.59 .019 .359 6.01 .003
ZVF1 .262 3.78 .053 .328 4.84 .008
ZVF2 -.085 .40 .529 -.021 .29 .752
ZVF3 .147 1.19 .277 .244 2.63 .073
ZVF4 .151 1.26 .264 .193 1.66 .192
ZVFT .377 7.83 .006 .264 4.53 .011
ZHS1 .132 .95 .331 .143 .90 .410
ZHS2 .097 .52 .471 .123 .68 .508
ZHS3 .238 3.11 .079 .373 6.20 .002
ZHS4 -.031 .05 .821 .122 1.53 .218
ZHS5 .161 1.42 .234 .226 2.25 .106
ZHF1 .106 .62 .431 .001 .38 .682
ZHF2 .332 6.07 .014 .320 4.98 .007
ZHF3 .056 .17 .677 .102 .47 .628
ZHF4 .159 1.39 .239 .172 1.41 .247
ZHFT .166 1.51 .221 .084 .74 .480
ZPS1 -.132 .96 .329 -.047 .74 .476
ZPS2 -.299 4.93 .027 -.229 2.32 .100
ZPS3 -.001 .00 .992 .149 1.69 .186
ZPS4
ZPS5
ZPF1 .075 .31 .580 -.096 1.31 .270
ZPF2
ZPF3 .068 .26 .613 .113 .56 .570
ZPF4 -.019 .02 .889 -.080 .41 .667
ZPFT .007 .00 .959 -.153 1.80 .168
TINCOM .875 4.87 .032
SINCOM .800 4.07 .049
TEXPND .869 4.80 .033
LCOST .781 3.88 .054
Legend:
DCC – discriminant correlation coefficients, F - F coefficient,
Sig. F - significance of F (post-hoc univariate differences)
• missing data signifies variables with no within-group variance
• (1) analysis Olympic : non-Olympic organisations
• (2) analysis Olympic : non-Olympic : Recreational organisations

9
The multivariate differences in financial indicators
(Table 4) were not statistically significant possibly also
due to the small number of units (55 sports), making
the ratio sample size : degrees of freedom un-
favourable. The univariate statistical differences, how-
ever, are all (with the exception of labour cost) statis-
tically significant and very large in absolute values
(Table 5).
DISCUSSION
One reason why sports organisations dealing pre-
dominantly with competitive sport (Olympic and non-
Olympic sports) base their seat in cities, while there
are more organisations dealing with recreational
sports in rural areas is probably in the better infras-
tructure in urban areas. Competitive, and especially
top-level sport, requires a critical mass of knowledge,
personnel, facilities, equipment etc, which is usually
only available in larger cities, especially in countries
with a smaller population. The second reason is prob-
ably in the number of possible members. Haggerty
and Denomme (1991) namely found that there is a
connection between the home-club distance and the
chosen club. Sports organisations are therefore moti-
vated to organise their activities close to their poten-
tial ”clients” and since cities have a greater popula-
tion, they are ”the place to be”. The young athletes
trying to become top level sportsmen are secondary
school pupils or students. Top level athletes in non-
commercial sports from the sponsors’ point of view,
which achieved marked success (rowing, kayak-ca-
noe, swimming, shooting…), but also most of the top
athletes in the commercial sports (skiing, track and
field) are also students. These come to the university
centres which are in cities, and the circle is closed.
However, the situation is somewhat different for
recreational-sport organisations. Recreational sport
should be accessible to all, no matter where they live.
In light of the finding by Haggerty and Denomme
(1991) it is logical for the recreational-sport organisa-
tions to operate locally, hence the even distribution of
such organisations between urban and rural areas.
One can only hope that this situation persists and that
these organisations will not be lured to the cities by
(false) hopes of higher revenues. Maybe the state will
be willing to systematically allocate some funds to
such organisations to keep carrying out their ”mis-
sion”.
The finding that the sports organisations dealing with
competitive sport are mostly larger than those dealing
with recreational sport is in accord with the findings
of Amis and Slack (1996), Kikulis, Slack and Hinings
(1995b) and Verhoven et al. (1997).
Olympic sports differ from the non-Olympic ones in
higher income, therefore it is normal that they also
have higher expenditures, since they are non-profit
organisations and must use all their income for the or-
ganisation’s activities. Olympic sports deal with a
much greater financial potential and it is therefore not
surprising that they have higher labour costs — 13.5 %
of the total compared to 7.2 %. This level of profes-
sionalism should not be ascribed only to the people
employed (cadre structure), but also to the greater
level of professionalism among the athletes. 
The finding that Olympic sports differ from the non-
Olympic ones in sales income is to be expected, since
the sports which are interesting for sponsors in
Slovenia are mostly Olympic sports (Bednarik et al.,
Bednarik, J., Ambro`i~, F., Mo~nik, R., Kolenc, M., & Kova~, M. (2000). Model Characteristics of… KinSI 6(1–2), 5–11
Table 5: Typical Olympic, non-Olympic and recre-
ational organisations
Variable Olympic Non-Olympic Recreational
ZVS1 2.53 2.45 2.50
ZVS2 .92 .58 .72
ZVS3 1.42 1.42 .58
ZVS4 4.99 2.63 .86
ZVS5 .47 .06 .06
ZVF1 1.14 .30 .18
ZVF2 .91 1.06 .91
ZVF3 4.74 3.02 1.91
ZVF4 2.77 1.24 .95
ZVFT .90 .21 .47
ZHS1 .82 .52 .41
ZHS2 .74 .22 .14
ZHS3 1.44 .82 .41
ZHS4 .61 .72 .06
ZHS5 .13 .03 .00
ZHF1 .01 .00 .01
ZHF2 .23 .07 .09
ZHF3 .14 .07 .02
ZHF4 .13 .03 .03
ZHFT .20 .10 .16
ZPS1 .01 .06 .02
ZPS2 .00 .03 .04
ZPS3 .33 .33 .05
ZPS4 .00 .00 .00
ZPS5 .00 .00 .00
ZPF1 .02 .01 .04
ZPF2 .00 .00 .00
ZPF3 .05 .03 .02
ZPF4 .01 .02 .03
ZPFT .06 .06 .27
TINCOME 255,390 46,820
SINCOME 165,635 35,530
TEXPEND 250,248 42,534
LCOST 43,472 3,967
Legend: all given values are arithmetic means of the relevant groups
Group centroids: 1 2 3
Olympic +.370 +.557 .274
Non-Olympic –.596 –.230 –.430
Recreational –.549

10
1998). It is interesting to note, on the other hand, that
sales as a percentage of total income is significantly
lower for Olympic sports (60.2 %) than for the non-
Olympic ones (70.4 %). This points to the fact that
the state financial support favours Olympic sports. The
data, however, shows that sports organisations in
Slovenia, similarly to EU (Andreff, 1994), are financed
predominantly from private sources.
The statistically significant differences in characteris-
tics, defined by the cadre structure according to the
European Classification of Professions (Camy and
Roux, 1997), which perform their work voluntarily (as
defined by Horch, 1994), part-time or full-time, show
that Slovene organisations have different cadre struc-
tures, depending on whether they deal mostly with
competitive or recreational sport.
The differences between competitive and recreation-
al sports organisations in the number of umpires and
coaches can be ascribed to the nature of their work,
this demands in competitive sport much more such
employees. The voluntary orientation of the umpires
is understandable as they perform their work just for
payment of expenses incurred. The statistically signif-
icant differences in voluntary and part-time coaches
show that a lot of coaching, probably mostly with the
younger athletes, is voluntary or part-time alongside
a regular full-time employment.
The differences between the Olympic and non-
Olympic organisations in full-time instructors lead us
to believe that the latter are forced to employ full-
time but also not-fully qualified coaches, because
there are not enough professionally trained (educat-
ed) ones available in Slovenia.
The organisations also differ in the number of medi-
cal, technical and administrative staff; This shows that
dealing with Olympic sports is more demanding than
with non-Olympic sports, and dealing with recre-
ational sport is even simpler. The voluntary work of
medical staff can be ascribed on one hand to some
sport-oriented (enthusiastic) doctors in Slovenia and
on the other hand to the publicity they gain from their
work with top athletes can then be exploited in their
regular job. The differences in the part-time adminis-
trative employees can be ascribed to rationality, since
part-time employees work (and are paid) only as
much as there is work to be done. However, the dif-
ferences in voluntary technical staff are harder to ex-
plain: one would not expect here persons searching
for personal satisfaction or ”fall-out effects” in their
full-time professional fields. The picture becomes
clearer if we take in consideration that sports organi-
sations in Slovenia are not the owners of the sports fa-
cilities (Bednarik, Petrovi} and [ugman, 1998). It is
therefore obvious that these are not technical facility
staff (of course employed full-time), but ancillary tech-
nical staff in the training and competitive processes.
These are usually parents or friends of the athletes.
Sports organisations should become more profes-
sional, in combination with volunteers (Schrodt,
1983; Beamish, 1985; Frisby, 1986; Macintosh, 1988;
MacMillan, 1991; Thibault, Slack and Hinings, 1991;
Auld and Godbey, 1998). This in consequence means
a lesser role for volunteers in the organisational pro-
cess (Slack, 1985; Macintosh, Bedecki and Franks,
1986; Slack and Thibault, 1988; Slack and Kikulis,
1989) and the transfer of the decision and manage-
ment function into professional hands. Since the de-
cision process is vital in any organisation (Knoke,
1981), predominantly voluntary management of
Slovenian sport could lead to its inefficiency.
However, Slovenia does have top level sports achieve-
ments which are comparable to other European coun-
tries when taking into account its population (Bed-
narik and Petrovi}, 1998; Bednarik, Petrovi} and
Nyerges, 1997). The percentage of sport-active is on
the same level as in other European countries (Bed-
narik and Petrovi}, 1998), sports organisations are fi-
nanced to about 75% from private sources and the
model of financing sport is very similar to the EU mod-
el as defined by Andreff (1994). It could be conclud-
ed that the effects of sport in Slovenia are relatively
good, either in spite of, or precisely because of vol-
untary management. This can also be because the
volunteers are more dedicated to their work than pro-
fessionals, and their greater possibility of making de-
cisions and personal connections are in a positive as-
sociation with their satisfaction in their work
(Chelladurai and Haggerty, 1991).
We can therefore conclude that sports organisations
in Slovenia have certain specific characteristics, which
also differ between Olympic and non-Olympic sports
and between non-Olympic competitive and non-
Olympic non-competitive sports. In any case, those
involved in managing sport should be aware of these
characteristics, take them into account and conform
accordingly.
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