- Access to this full-text is provided by Springer Nature.
- Learn more
Download available
Content available from International Entrepreneurship and Management Journal
This content is subject to copyright. Terms and conditions apply.
The impact of selected factors on new business formation
in the private healthcare sector
Toma s z S k i c a
1
&Teresa Mroczek
2
&
Małgorzata Leśniowska-Gontarz
3
Published online: 11 July 2018
#The Author(s) 2018
Abstract Numerous factors influence the development of the private healthcare sector.
Therefore, the selection of these factors, which represent a potential opportunity for
forming new entities, is a crucial from the point of view of entrepreneurship. In our
research we selected strategic variables which have got direct influence on entrepre-
neurship in the private healthcare sector in Poland. Theoretical approach was based on
literature review which have revealed the main factors and their relationships with
entrepreneurship according to the previous research studies. This research study was
conducted for the entire population of municipalities in Poland. Methodology was
based on Intelligent Data Analysis (IDA) which can be applied for a large amounts of
data in order to extract useful knowledge from it. Moreover, in research study were
applied explanation techniques –decision rules –in order to indicate, to what extent the
environment have influence on strategic choices conditioning the success of businesses.
The results have revealed that it is possible to determine a set of the most important
factors influencing entrepreneurship in the private healthcare sector in Poland. On the
other hand, were indicated these variables which do not participate in process of
influencing on entrepreneurship in private healthcare sector in Poland.
Keywords Healthcare .Entrepreneurship .Private sector .Firm .Factors
JEL classification I10 .D00 .L26
Int Entrep Manag J (2019) 15:307–320
https://doi.org/10.1007/s11365-018-0530-7
*Małgorzata Leśniowska-Gontarz
mlesniowska@wsiz.rzeszow.pl
1
Institute for Financial Research and Analyses / Department of Finance, University of Information
Technology and Management in Rzeszow (UITM), Rzeszów, Poland
2
Department of Expert Systems and Artificial Intelligence, University of Information Technology
and Management in Rzeszow (UITM), Rzeszów, Poland
3
Institute for Financial Research and Analyses / Department of Management, University of
Information Technology and Management in Rzeszow (UITM), Rzeszów, Poland
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Introduction
Socio-economic changes in society have become a driving factor of rising demand for
healthcare services. The objective deficit of public healthcare entities has led to
dynamic development in the private healthcare sector, especially in the area of new
private medical businesses. The environment for private medical businesses consists of
factors having influence on new business formation, in both positive and negative
ways. Hence, there are questions not only about overcoming negative factors perceived
as threats, but also about the factors perceived as opportunities for formation of new
private medical businesses.
The main purpose of this research study is to identify strategic factors which have
direct influence on entrepreneurship in the private healthcare sector. The complexity of
the phenomenon imposes implementation of an unconventional approach in this field
of exploration.
Our approach is based on Intelligent Data Analysis (IDA) - a methodology that
includes a set of techniques that can be applied for extracting useful knowledge from
large amounts of data. In order to indicate the most important factors of new business
formation in the private healthcare sector, were applied explanation techniques –
decision rules –to express mentioned relationships.
There search allowed us to identify and describe the variable that plays the crucial
role in explaining the reasons new firms are established in the healthcare sector. The
study links the explanatory variables with the type of municipality, and provides the
answer to the question of which factors are responsible for entrepreneurship in the
private healthcare sector due to the municipality type. Moreover, the results proves that
slightly different factors are responsible for successful entrepreneurship support in
municipalities with different numbers of already existing private healthcare entities.
Summarizing, this study showed what variables are important for the given category
of muncipality and for the level of entrepreneurship in that municipality.
Literature review
It is widely recognized that the success and vitality of entrepreneurship are essential
factors in measuring an economy’s progress, its quality and its future expectations.
Entrepreneurship is closely related to SMEs (Small & Medium Enterprises) and large
companies in local, regional, national or international markets, in private and public
organizations and helps lead to competitiveness in the face of the effects of globaliza-
tion. Entrepreneurial activities are important in creating new economic activities which
in turn increases innovation, employment, economic wealth and growth, consolidates
competitiveness in advanced economies and assures social welfare in less economically
developed countries (Audretsch et al. 2005,p.5).
Entrepreneurship is one of the most important forces shaping the changes in an
economic area, regardless of whether it occurs within the framework of the formal
structure of the economy or takes place informally outside state regulatory systems
(Carree and Thurik 2010;Thuriketal.2002; Williams and Nadin 2010 etc.).
Numerous definitions of entrepreneurship are used in the literature. P. Drucker’s
definition of entrepreneurship is: an act of innovation that involves endowing existing
308 Int Entrep Manag J (2019) 15:307–320
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
resources with new wealth-producing capacity (Drucker 1985). S. Shane and S.
Venkataraman define entrepreneurship as the issue which involves the nexus of two
phenomena: the presence of lucrative opportunities and the presence of enterprising
individuals (Shane and Venkataraman 2000, p. 218). In Williams and Thomson’s
(1998) definition, entrepreneurship is related to productivity and it is assumed that
the entrepreneurs are responsible for determining optimal production, investment, and
financing decisions. For J.A. Schumpeter, the entrepreneur was identified with the
function of carrying out new transformations and combinations which were usually
embodied in new firms, which arise not out of the existing firms but grow up beside
them (Schumpeter 1961,pp.66–78).
Starting a business is not an event, but a process which may take many years to
evolve and come to fruition. Entrepreneurial research has developed along two main
lines:
(1) the personal characteristics or traits of the entrepreneur; and
(2) the influence of social, cultural, political and economic contextual factors
(Mazzarol et al. 1999, p. 49) initial approach, including the personal characteris-
tics, was conducted by A.T. Robinson and L.D. Marino. Firstly, research study
gives empirical evidence that overconfidence is significantly related to venture
creation decisions. According to the results, when overconfidence increases,
venture creation decisions will increase as well. Secondly, the relationship be-
tween overconfidence and venture creation decisions is partially mediated by risk
perceptions (Robinson and Marino 2015).
F. Miralles et al. conducted research study related to the topic topic: how individuals
engaged in the actual behavior could provide differences in the perceptions and other
intention’s antecedents? The results are as follows: actual behavior could be a source of
differences across individuals, specifically if we also take into consideration different
age brackets. The findings suggest that being exposed to the actual behavior of
entrepreneurship would strengthen the influence of personal attitude and perceived
behavioral control on entrepreneurial intention for younger individuals, meanwhile, it
would weaken the relationship between perceived behavioral control and entrepreneur-
ial intention for older individuals (Miralles et al. 2017, p. 899). Similar research studies
dedicated to individual characteristics of entrepreneurs (including background such as
gender, age, civil status, educational level, entrepreneurial culture and other important
success factors), was conducted by Stringa et al. (2009) or Orlandi (2017).
Theory development and research into the relationship between the environment and
organisation formation is a more recent event. Advocates of this approach believe that
the entrepreneurial trait perspective has reached a dead end (Aldrich 1990) and has
partially contributed to the understanding of new firm formation. The study of the role
of the environment, the so-called rates or demand perspective (Peterson 1980;
Richardson 2001; Richardson and Peacock 2006), is seen as a more viable approach.
While not denying the role played by the founders’characteristics, the demand
perspective proposes that the environment is more important in understanding organi-
sation formation.
The environment plays a crucial role in the formation of entrepreneurship.
Timmons’(1989) paper suggests that external factors have impact on the success of
Int Entrep Manag J (2019) 15:307–320 309
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
entrepreneurship. Furthermore, he assumes that the key to successful entrepreneurship
is determining and applying the opportunities and being able to match the situation and
organization to the important players. In turn, Kuratko and Hodgetts (1998)assumethat
entrepreneurship is made up of multidimensional processes including the impact on
environment (internal and external as well), organizations, and individuals. According
to their concept, the external environment consists of two parts: the societal environ-
ment (including economic, political, legal and technological forces), and task environ-
ment (which is related to the specific industry environment).
According to societal environment E. Hormiga and A. Bolívar-Cruz have examined
the question of whether the ‘migrant condition’(that is, the experience of being an
immigrant) has an impact on the perception of the risks involved in engaging new
business activity. The results are as follows: immigrants are less likely to perceive risk
in making a new business than natives. What is more, the results have given the picture
of negative relationships between the perception of risk and formation a new business,
thus confirming that tolerance to risk is a crucial characteristic of entrepreneurs
(Hormiga and Bolívar-Cruz 2014,p.313).
P. A. Nylund and B. Cohen in their paper indicates that collision density is indeed a
crucial factor for the development of entrepreneurial ecosystems. In this work, collision
density was defined as the potential frequency of interdisciplinary interactions to
explain dynamic growth of entrepreneurial ecosystems (Nylund and Cohen 2017).
The multidimensionality of the definition of entrepreneurship can be found in both:
the way it is determined and in the way it is measured. A frequently implemented
approach assumes using economic definitions of entrepreneurship based on two func-
tions: the entrepreneur and the perception of economic opportunities and innovations.
In turn, the second approach assumes the use of those definitions from the managerial
world, where entrepreneurship is related to a way of managing. Referring to the second
area of multidimensionality (measurement), two approaches are suggested: static and
dynamic perspective. Business ownership and self-employment are frequently consid-
ered equivalent of entrepreneurship and those types of measures can be the basis for
static indicators (Carree et al. 2002; Uhlaner and Thurik 2007). From the point of view
of the second perspective (dynamic), the proposed measures of entrepreneurship are
based on latent (preference), nascent and start-up activity (Grilo and Irigoyen 2006).
The healthcare sector is similar to others in the area of environment conditions,
structure and strategies. Detailed commonalities of the healthcare sector and others in
the area of environment include turbulence, inflexibility, and high competitiveness. In
turn, structural similarities include new entrants, mergers and consolidation. The latter
strategies have moved in the direction of cost accounting and strategic alliances.
Accordingly, the healthcare sector is determined by unstable and ruthless environment
circumstances. In light of these environment variables, healthcare has undergone
structural and strategic changes and innovations to achieve organizational economies
of scale, improve utilization of resources, enhance access to capital, increase political
power and extend the scope of the market (Zuckerman et al. 2000).
Entrepreneurship research studies are highly recommended for the healthcare sector,
as nowadays owners of medical entities perform entrepreneurial activities in order to
generate innovative strategies and achieve a competitive advantage in the conditions of
the turbulent environment. Chicken (2000) clarifies that businesses conduct entrepre-
neurial accomplishments for the exploitation of revenues or benefits. In private entities,
310 Int Entrep Manag J (2019) 15:307–320
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
entrepreneurial actions affect profit measured by monetary terms. As further healthcare
entities convert to for-profit status, entrepreneurial activities would arise when they
compete for market share or profit. In not-for-profit healthcare organizations, the
benefit of medical treatment can be seen through the prism of organizational existence,
reputation, development and chances. These circumstances also involve multidimen-
sional strategies and necessitate the implementation of entrepreneurship in healthcare
entities. Moreover, the complex healthcare environment needs more inventive solu-
tions. Therefore, healthcare entities are beginning to exploit entrepreneurship in their
management techniques.
An example of the applicability of entrepreneurship to the healthcare sector was
described by Chicken (2000). He offers a number of entrepreneurial activities for a
range of sectors. For instance, he finds these activities in financial services (banking
and insurance sectors), manufacturing, agriculture, transportation, mining, fishing,
hotels, media, civil services and government. He further summarizes that entrepreneur-
ial activities occur under three circumstances. First, operations must be carried out in
the open market. Second, some operations must be funded or subsidized by govern-
ment. Third, operations could be completely funded by government. Using this formu-
la, it is clear that entrepreneurial activities can occur in the healthcare sector, since
healthcare organizational activities satisfy the first two criteria (Guo 2003,p.50).
To sum up, multidimensional phases are required to assess the environment and
organization prior to making changes implementing innovative strategies. Indeed,
entrepreneurship is applicable to the healthcare sector as it has been successfully
utilized in other sectors. It can be identified as a gap between the theory of entrepre-
neurship in healthcare organizations and research studies in the area of formation. This
research study, therefore, aims to fill the research gap (between theory and practice), by
exploring the set of factors affecting new business formation in the private healthcare
sector giving the answer to the question of what are the most important factors of new
business formation in healthcare, especially in the private sector. To do this factors were
applied with the explanation technique - rule induction.
Rule induction
Rule induction –one of the fundamental tools of Data Mining –allows for easy
interpretation of dependences hidden in data. Usually rules are expressed in the
following form:
IF attribute1;value1
ðÞAND…AND attributen;valuen
ðÞTHEN decision;valuesðÞ:ð1Þ
Data from which rules are induced are usually presented in a form of decision table
(Pawlak 1982).Rows of the table represent nonempty and finite a set of cases (also
called as objects or examples), while columns represent nonempty and finite set of
variables. Each variable has a finite set of values. Independent variables are called
attributes and a dependent variable is called a decision. The set of objects with the same
value of decision attribute is called a decision class (or concept).
Construction of elementary conditions (attribute
i
,value
i
)in(1)maybevariousand
depends on a rule induction algorithm. The most popular technique is rule induction
Int Entrep Manag J (2019) 15:307–320 311
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
using a sequential covering algorithm (Clark and Niblett 1989; Han and Kamber 1986)
which creates such a number of rules to assure that every object in the training data Bis
covered^by at least one rule. Other techniques are connected with the induction based
on rough set theory (Pawlak 2002;Grzymała-Busse and Yao 2011) or induction based
on other formalism of knowledge representations (Quinlan 1986; Cohen 1995;
Carvalho and Freitas 2004; Mroczek and Hippe 2015). In general, the aim of majority
of rule induction algorithms is to find the minimal set of classification rules which
cover and correctly predict decision classes of a given set of examples.
Rule quality measures
Many measures of rule quality assessment concern the relation between a decision rule
and a class. Examples satisfying all elementary conditions are assigned to the concept
indicated in the rule conclusion. The positive objects are those belonging to the
decision class pointed out in the rule conclusion. The negative objects are the remaining
ones. The relations can be presented in the form of a contingency table.
Let p denote the number of positive examples covered by the rule and P denote all
positive examples in the training set. Let n denote the number of negative examples
covering the rule and N denote all negative examples. The contingency matrix for rule
has the following form:
where p + n –is the number of objects which recognize the rule; P + N -p - n is the
number of objects which do not recognize the rule; P is the number of objects which
belong to the decision class described by the rule and N is the number of objects which
do not belong to the decision class described by the rule.
There are two basic rule quality measures - accuracy and coverage:
Acc ¼p
pþnð2Þ
Cov ¼p
Pð3Þ
The accuracy reflects the correctness of the rule, the coverage reflects the applica-
bility of the rule. Both measures are not independent of each other and when considered
simultaneously give the complete view of rule quality. It is desirable for a rule to be
accurate as well as to have a high degree of coverage. But with the increase of accuracy
the rule coverage decreases. Therefore, to define the rule quality measures a large
number of tests is required taking into account the accuracy and coverage at the same
time. Taking into consideration the origin of data in the experiments additionally rule
quality measure was used.
312 Int Entrep Manag J (2019) 15:307–320
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
An entropy of a variable v (attribute or decision) with values v
1
,v
2
,...,v
n
is defined
by the following formula:
Info UðÞ¼−∑n
i¼1pv
i
ðÞ∙logpv
i
ðÞ ð4Þ
where U is the set of all cases in a data set and p(v
i
) is a probability (relative frequency)
of value vi in the set U, i=0;1,…, n. Entropy of a set is understood as the number of
information points necessary to communicate whether a certain training object belongs
or not to the decision class described by the rule. Whereas the number of information
points necessary to communicate whether certain object is or is not recognized by the
rule is the conditional entropy of the decision d given an attribute a is:
Info djaðÞ¼−∑m
j¼1pa
j
∙∑n
i¼1pd
ijaj
∙logpd
ijaj
ð5Þ
where a
1
,a
2
,.., a
m
are all values of a and d
1
,d
2
,…,d
n
are all values of d.
Rule induction algorithm
A modified version of Quinlan’s classification model (Quinlan 1986), called C5.0, was
used for rule induction (generation). The algorithm splits the objects maximizing the
information gain. Information gain is based on the idea of entropy, a measure of
uncertainty from information theory. Each subsample defined by the first split is then
split again and the process repeats until the subsamples cannot be split any further.
Finally, the lowest-level splits are reexamined, and those that do not contribute
significantly to the value of the model are removed or pruned (Pang and Gong 2009;
Pandya and Pandya 2015).
Data and methodology
The research study covered a period of six years. The base year was 2011. The data
source was publicly available statistics of the Local Data Bank (LDB) of the Central
Statistical Office (CSO). The base year was selected because of the fact the Act of 15
April 2011 on medical activity (the Act of 15 April 2011 r., on medical activity,
Dz.U.2016 poz. 1638) modifying the organization of the Polish healthcare sector began
to apply. The last year of the analysis was 2016, because it was the last year for which
statistics were available.
The research was designed for the entire population of municipalities in Poland. Munic-
ipal level government offers the broadest instruments supporting entrepreneurship. Accord-
ing to this, the scale of the impact on the bottom-up opportunities to create entrepreneurship
is the largest (Bania and Dahlke 2014; Kogut-Jaworska 2008;Wyszkowska2012). Their
number according to the state of 2011 was 2479–306 urban municipalities, 1571 rural
municipalities and 602 urban-rural municipalities (available at: http://eteryt.stat.gov.
pl/eteryt/raporty/WebRaportZestawienie.aspx, date of access 1st October 2017).
Finally, due to the lack of data for the analysis, 2408 municipalities were selected.
Int Entrep Manag J (2019) 15:307–320 313
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The research sample was formed by all the municipalities in Poland, for which data were
available in 2011–2016 to describe entrepreneurship in the private healthcare sector. Taking
into account the above criteria, the estimations were made on a sample of 302 urban
municipalities, 602 urban-rural municipalities and 1504 rural municipalities.
The explained variable (decision) was entrepreneurship in the private healthcare
sector described by the indicator defined as the number of newly registered private
economic entities in Section Q
1
in relation to the working-age population in the
municipality. Division on a sample of 2408 cases was carried out due to the
value of the entrepreneurship indicator. Taking into account the accepted criteria
in the classification of municipalities allowed us to prepare the division of the
examined units in a way that provided the individualizing of single municipalities
(Table 1).
The division was conducted using two criteria: a) the value of the indicator of the
entrepreneurship, b) type of municipalities (urban, urban-rural, rural). The argumenta-
tion for such a division is as follows: it is impossible to evaluate equally low levels of
entrepreneurship in urban and rural municipalities. A similar situation can be observed
when we have two municipalities: with low and high levels of entrepreneurship, where
respectively the first one is a rural municipality and the second is a big city. Among
municipalities a separate division was created for large cities (Warsaw, Krakow, etc.)
(above 1,00). Their exclusion was dictated by the very high value of the analysed
indicator implying correct interpretation.
The group of explanatory variables consistedof16factorsdividedintothree
groups (Table 2). The first one consisted of variables referring to the municipal
budget policy (variables numbered from 1 to 6 inclusive). The second group
included social variables. They covered variables numbered from 7 to 13
(inclusive). The third group represented economic variables expressed by factors
numbered from 14 to 16 (inclusive). The last explanatory variable was the
municipalities category.
The goal of our study was to identify strategic variables which have direct influence
on entrepreneurship in the private healthcare sector. To this end we applied a method-
ology based on rule induction. The C5.0 algorithm –capable of generating rules - was
used in this study. In the first step we used the MultipleScanning method in order to
discretize numerical valuables, where during every scan the entire attribute set is
analyzed. For all attributes the best cut-point is selected. This process continues until
the same stopping criterion is satisfied. Although the C5.0 algorithm has an internal
discretization mechanism the Multiple Scanning discretization technique is significant-
ly better than the one used in C5.0 (Grzymała-Busse and Mroczek 2016). Then we
induced rules and examined their effectiveness using a ten-cross validation procedure.
To this end all cases were randomly re-ordered, and then a set of all cases was divided
into ten mutually disjoint subsets of approximately equal size. All but one subsets were
used for rule induction, while the remaining one was used for testing. Finally, we
conducted a qualitative analysis of the generated rules and identified the strategic
variables.
1
Section Q of the Polish Classification of Activities (PCA) 2007 classification includes health care and social
services and its section 86 healthcare. Private entities were isolated from this section for the purposes of the
research study.
314 Int Entrep Manag J (2019) 15:307–320
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Results
The discretization method, Multiple Scanning, was applied to a data set with the level
of consistency equal to 100%. The minimal and stable error rate (19.93%) was obtained
for the 3rd scan. Discretized data were used to induce rules using C5.0 algorithm.
Tab le 3shows the number of rules in each of the categories of municipalities.
Tab l e 1 Number of municipalities according to the divisions of entrepreneurship indicator in 2011–2016
Divisions Number of municipalities
2011 2012 2013 2014 2015 2016
from 0,00 to 0,02 1167 1164 1215 1155 1202 1182
from 0,02 to 0,20 1007 1027 997 1042 1009 1035
from 0,20 to 0,50 169 155 137 149 143 137
from 0,50 to 1,00 39 36 31 35 26 30
Above1,00 262628272824
Sum 2408 2408 2408 2408 2408 2408
Source: Own work
Tab l e 2 Explanatory variables used in estimation
Order number Name of variable Abbreviation
1 Share of assets expenditures in total expenditures (in %) Assets_exp_in_tot
2 Share of healthcare expenditures in total expenditures (in %) Health_exp_in_tot
3 Total expenditures of the municipality per capita (inPLN) Exp_per_capita
4 Total income of the municipality per capita (in PLN) Income_per_capita
5 Total EU funds per capita (in PLN) EU_funds_per_capita
6 Own income of municipality per capita (in PLN) O_income_per_capita
7 Population in pre-production age to total population (in %) Pop_in_pre-prod
8 Population in production age to total population (in %) Pop_in_prod
9 Population in post-production age to total population (in %) Pop_in_post-prod
10 Internal migration balance (difference between the flow and
outflow of people from the municipality)
Balance_migration
11 Number of people per 1 km2 (population density in the municipality) Population
12 Number of medical advisories given within a year Medical
13 Number of working people per 1000 population Working
14 Number of newly registered business entities to the production-age
population (entrepreneurship indicator)
Newly_entities
15 Number of operating business entities in Q section in total (public
and non-public)
Entities_sec_Q
16 Category of the municipality (1- urban municipality, 2- rural
municipality, 3- urban-rural municipality)
Categ_Mun
Source: Own work based on Local Data Bank
Int Entrep Manag J (2019) 15:307–320 315
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The efficiency of the rule sets examined using a ten-cross validation procedure was
73%. Qualitative analysis of the rules allowed us to define a set of the most important
variables (from the classification point of view) for each division of the explained
variable (see Figs. 1–3). The number of attributes has been normalized. Whereas the
distribution of results into three figures results from the value distribution of the
entrepreneurship indicator in each of the year. Analysis of the value distribution of
the indicator (see Table 1), allows us to combine in the Fig. 1municipalities with
indicator values from 0.00 to 0.02 and from 0.02 to 0.20. Figure 2shows the
municipalities with the value indicator from 0.20 to 0.50. Finally, in Fig. 3there are
municipalities with the value of the indicator tested from 0.50 to 1.00 and above 1.00.
The results of show that for the values of the entrepreneurship indicator from 0.00 to
0.02 and from 0.02 to 0.20 can indicate common variables that have the greatest impact
on entrepreneurship. These are: market saturation with business entities in Q section in
total and share of assets expenditures of the municipalities in total expenditure.
Moreover, in the lowest level of the indicator, its value is determined by: the number
of employees, the production-age population, the number of newly registered business
entities, the population, the migration balance and the total income of municipalities per
capita. On the other hand, where the value of the indicator is maintained in division
from 0.02 to 0.20 the significance of explanation of entrepreneurship was: the share of
Tab l e 3 Number of rules for each of the divisions of entrepreneurship indicator
Values divisions of entrepreneurship indicator Number of rules for each of the divisions
from 0,00 to 0,02 26
from 0,02 to 0,20 94
from 0,20 to 0,50 23
from 0,50 to 1,00 13
Above 1,00 8
Source: Own work
12%
0% 4%
15%
8% 12%
69%
8%
15%
4%
12% 8%
31%
4% 4%
12%
23%
7% 10% 12% 6%
23%
64%
17% 13% 7% 13%
20%
54%
10% 7%
22%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Income_per_capita
O_income_per_capita
Pop_in_post-prod
Pop_in_prod
Pop_in_pre-prod
Newly_entities
Entities_sec_Q
Medical
Working
Categ_Mun
Balance_migration
EU_funds_per_capita
Assets_exp_in_tot
Exp_per_capita
Health_exp_in_tot
Population
from 0,00 to 0,02 from 0,02 to 0,20
Fig. 1 Attributes appearing in the rules explaining entrepreneurship for divisions from 0.00 to 0.02 and from
0.02 to 0.20. Source: Own work
316 Int Entrep Manag J (2019) 15:307–320
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
assets expenditures of municipalities in total expenditures, the number of newly
registered business entities, the total income of municipalities per inhabitant and the
population.
When the value of entrepreneurship indicator was in the division from 0.20 to 0.50
the most significant variables where: population density, total expenditure of munici-
palities per capita, number of medical advisories given within a year and value of EU
funds per capita and population in pre-production age.
For higher (from 0.50 to 1.00) and the highest (above 1) entrepreneurship, the most
important variable was the population. In addition, to the indicator from 0.50 to 1.00,
the remaining significant explanatory variables were: total expenditure of municipali-
ties per capita, population in the post-production and pre-production age, and total level
of income of municipalities per capita. On the other hand, the highest rate of entrepre-
neurship (in addition to the above-mentioned population), was explained by: total
income and expenditure of municipalities per capita, pre-production population, num-
ber of employed persons and share of municipalities’expenditures on healthcare sector
in total expenditures.
17%
0%
22%
13%
26%
0%
22%
39%
13%
0%
13%
26%
13%
48%
9%
87%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Income_per_capi
ta
O_income_per_c
apita
Pop_in_post-prod
Pop_in_prod
Pop_in_pre-prod
Newly_entities
Entities_sec_Q
Medical
Working
Categ_Mun
Balance_migratio
n
EU_funds_per_c
apita
Assets_exp_in_to
t
Exp_per_capita
Health_exp_in_to
t
Population
from 0,20 to 0,50
Fig. 2 Attributes appearing in the rules explaining entrepreneurship for division from 0.20 to 0.50. Source:
Own work
46%
0%
46%
0%
46%
0% 8%
23% 15%
0%
23%
8%
23%
46%
23%
85%
50%
0%
13%
0%
38%
0%
13%
0%
25%
0%
13% 13% 13%
38%
25%
100%
0%
20%
40%
60%
80%
100%
120%
Income_per_capita
O_income_per_capita
Pop_in_post-prod
Pop_in_prod
Pop_in_pre-prod
Newly_entities
Entities_sec_Q
Medical
Working
Categ_Mun
Balance_migration
EU_funds_per_capita
Assets_exp_in_tot
Exp_per_capita
Health_exp_in_tot
Population
from 0,50 to 1,00 above 1,00
Fig. 3 Attributes appearing in the rules explaining entrepreneurship for divisions from 0.50 to 1.00 and above
1.0. Source: Own work
Int Entrep Manag J (2019) 15:307–320 317
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Conclusion
Let us recall that our main objective was to identify strategic variables which have
direct influence on entrepreneurship in the private healthcare sector. The results of our
experiments show that it is possible to identify a set of the most important variables
influencing entrepreneurship in the private healthcare sector. The level of the indicator
of a lower level of entrepreneurship (divisions I and II), to the greatest extent was
explained by the number of currently active business entities in Q section as well as the
assets expenditure of municipalities in total expenditure. Entrepreneurship in munici-
palities with a higher level of entrepreneurship (divisions III and IV), to the greatest
extent was explained by the population and total expenditures of the municipality per
capita. In the municipalities with the highest entrepreneurship, this was the population
and the total income of municipalities per capita. It is worth emphasizing that the
variable expenditure of municipalities per inhabitant was found in these municipalities
in the third place among all determinants.
At the same time, the results of the research have identified a set of variables that are
not relevant to the explanation of entrepreneurship. For the entrepreneurship indicator
from 0.00 to 0.02 this was the variable own income of municipalities per capita. On the
other hand, the value of the indicator from 0.20 to 0.50 was the number of newly-
opened business entities, the type of municipality and the share of own income in the
total income of municipalities. In municipalities with a value of indicator from 0.50 to
1.00 they were: production age population, number of newly-opened business entities,
type of municipality and share of own income in total incomes of municipalities.
Finally, for the highest value of the indicator, entrepreneurship was not explained by:
population in production age, number of medical advisories, number of newly-opened
business entities, type of municipalities and share of own income in total incomes of
municipalities. Exceptions were cases of entrepreneurship level from 0.02 to 0.20 in
which all variables determined the level of the indicator. That means that the process of
explanation in this division of entrepreneurship in the private healthcare sector is
complicated and complex.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International
License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and repro-
duction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were made.
References
Aldrich, H. (1990). Using an ecological perspective to study organizational founding rates. Entrepreneurship
Theory and Practice, 14(3), 7–24. https://doi.org/10.1177/104225879001400303.
Audretsch, D. B., Castrogiovanni, G. J., Ribeiro, D., & Roig, S. (2005). Linking entrepreneurship and
management: Welcome to the international entrepreneurship and management journal. International
Entrepreneurship and Management Journal, 1(1), 5–7.
Bania, M., Dahlke, P. (2014). Samorząd terytorialny jako potencjalne źródło wsparcia sektora MSP na
przykładzie Wielkopolski. In Mierzejewski, D.J., Polcyn J. (Eds.), Gospodarka w warunkach integracji
(pp. 69–85), Czwartki u ekonomistów, t. II, Wydawnictwo Państwowej Wyższej Szkoły Zawodowej im.
Stanisława Staszica w Pile.
318 Int Entrep Manag J (2019) 15:307–320
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Carree, M. A., & Thurik, A. R. (2010). The impact of entrepreneurship on economic growth. In Z. J. Acs & D.
B. Audretsch (Eds.), Handbook of entrepreneurship research: An interdisciplinary survey and introduc-
tion (pp. 557–594). New York: Springer.
Carree, M. A., van Stel, A. J., Thurik, A. R., & Wennekers, A. R. M. (2002). Economic development and
business ownership: An analysis using data of 23 OECD countries in the period 1976–1996. Small
Business Economics, 19(3), 271–290.
Carvalho, D. R., & Freitas, A. A. (2004). Hybrid decision tree/genetic algorithm method for data mining.
Information Sciences, 163(1–3), 13–35. https://doi.org/10.1016/j.ins.2003.03.013.
Chicken, J. C. (2000). Management and entrepreneurism. London: Thomson Learning.
Clark, P., & Niblett, T. (1989). The cn2 induction algorithm. Machine Learning, 3(4), 261–283.
Cohen, W. W. (1995). Fast effective rule induction. In Proceedings of the twelfth international conference on
machine learning (pp. 115–123). Tahoe City: Morgan Kaufmann.
Drucker, P. F. (1985). Innovation and entrepreneurship. New York: Harper and Row.
Grilo, I., & Irigoyen, J. M. (2006). Entrepreneurship in the EU: To wish and not to be. Small Business
Economics, 26(4), 305–318.
Grzymała-Busse, J. W., & Mroczek, T. (2016). A comparison of four approaches to discretization based on
entropy. Entropy, 18(3), 69. https://doi.org/10.3390/e18030069.
Grzymała-Busse, J. W., & Yao, Y. Y. (2011). Probabilistic rule induction with the LERS data mining system.
International Journal of Intelligence Systems, 26(6), 518–539.
Guo, K.L. (2003). Applying entrepreneurship to healthcare organizations. New England Journal of
Entrepreneurship, 6(1) Article 8. Available at: http://digitalcommons.sacredheart.edu/neje/vol6/iss1/8.
Han, J., & Kamber, M. (1986). Data mining: Concepts and techniques. San Francisco: Morga n Kaufmann.
Hormiga, E., & Bolívar-Cruz, A. (2014). The relationship between the migration experience and risk
perception: A factor in the decision to become an entrepreneur. International Entrepreneurship and
Management Journal, 10(2), 297–317.
Kogut-Jaworska, M. (2008). Instrumenty interwencjonizmu lokalnego w stymulowaniu rozwoju
gospodarczego. Warszawa: CeDeWu.
Kuratko, D. F., & Hodgetts, R. M. (1998). Entrepreneurship: A contemporary approach (4th ed.). Fort Worth:
Dryden Press.
Mazzarol, T., Volery, T., Doss, N., & Thein, V. (1999). Factors influencing small business start-ups. A
comparison with previous research. International Journal of Entrepreneurial Behaviour & Research,
5(2), 48–63.
Miralles, F., Giones, F., & Gozun, B. (2017). Examining the consequences of current entrepreneurial behavior
on entrepreneurial intention. International Entrepreneurship and Management Journal, 13(3), 881–903.
Mroczek, T., & Hippe, Z. S. (2015). Conversion of belief networks into belief rules: A new approach. In
Proceedings of the 9th international conference on computer recognition systems CORES (pp. 91–100).
Nylund, P. A., & Cohen, B. (2017). Collision density: Driving growth in urban entrepreneurial ecosystems.
International Entrepreneurship and Management Journal, 13(3), 757–776.
Orlandi, L. B. (2017). Am I an entrepreneur? Identity struggle in the contemporary women entrepreneurship
discourse. Contemporary Economics, 11(4).
Pandya, R., & Pandya, J. (2015). C5.0 algorithm to improved decision tree with feature selection and reduced
error pruning. International Journal of Computer Applications, 11(16), 18–21.
Pang, S. L., & Gong, J. Z. (2009). C5.0 classification algorithm and application on individual credit evaluation
of banks. Systems Engineering-Theory & Practice, 29(12), 94–104.
Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11,41–356.
Pawlak, Z. (2002). Rough sets and intelligent data analysis. Information Sciences, Elsevier, 147,1–12.
Peterson, R. A. (1980). Entrepreneurship and organization. In P. C. Nystrom & W. H. Starbuck (Eds.),
Handbook of organization design. Oxford: Oxford University Press.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1,81–106.
Richardson, J. (2001). Supply and demand for medical care: Or, is the healthcare market perverse? Australian
Economic Review, 34(3). https: //doi.o rg/1 0.1111/1467-84 62.00203.
Richardson, J., & Peacock, S. J. (2006). Supplier-induced demand. Applied Health Economics and Health
Policy, 5(2), 87–98.
Robinson, A. T., & Marino, L. D. (2015). Overconfidence and risk perceptions: Do they really matter for
venture creation decisions? International Entrepreneurship and Management Journal, 11(1), 149–168.
Schumpeter, J. A. (1961). The theory of economic development. New York: Oxford University Press.
Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of
Management Review, 25(1), 217–227.
Int Entrep Manag J (2019) 15:307–320 319
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Stringa, A., Sallaku, S., & Tabaku, J. (2009). Individual characteristics of entrepreneurs in transition countries.
The Albanian case. Współczesna Ekonomia, 2(10), 91–101.
Thurik, R., Wennekers, S., & Uhlaner, L. M. (2002). Entrepreneurship and economic performance: A macro
perspective. International Journal of Entrepreneurship Education, 1(2), 157–179.
Timmons,J.A.(1989).The entrepreneurial mind. MA: Brick House Publishing.
Uhlaner, L. M., & Thurik, A. R. (2007). Postmaterialism influencing total entrepreneurial activity across
nations. Journal o f Evolutionary Economics, 17(2).
Williams, C., & Nadin, S. (2010). Entrepreneurship and the informal economy: An overview. Journal of
Developmental Entrepreneurship, 15(4), 361–378.
Williams, E. E., & Thompson, J. R. (1998). Entrepreneurship and productivity. New York: University Press of
America.
Wyszkowska, D. (2012). Jednostki samorządu terytorialnego w tworzeniu warunków do rozwoju
przedsiębiorczości i innowacyjności na przykładzie gmin województwa podlaskiego. In J. Witek (Ed.),
Przedsiębiorczość szansąrozwoju regionu. Tom I. Polityka wsparcia i kreowania przedsiębiorczości (pp.
255–268). Zeszyty Naukowe Uniwersytetu Szczecińskiego: Szczecin.
Zuckerman, H. S., Dowling, W. L., & Richardson, M. L. (2000). The managerial role. In S. M. Shortell & A.
D. Kaluzny (Eds.), Healthcare management: Organization design and behavior (4th ed., pp. 34–60).
Albany: Delmar Publishers.
320 Int Entrep Manag J (2019) 15:307–320
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center
GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers
and authorised users (“Users”), for small-scale personal, non-commercial use provided that all
copyright, trade and service marks and other proprietary notices are maintained. By accessing,
sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of
use (“Terms”). For these purposes, Springer Nature considers academic use (by researchers and
students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and
conditions, a relevant site licence or a personal subscription. These Terms will prevail over any
conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription (to
the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of
the Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may
also use these personal data internally within ResearchGate and Springer Nature and as agreed share
it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not otherwise
disclose your personal data outside the ResearchGate or the Springer Nature group of companies
unless we have your permission as detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial
use, it is important to note that Users may not:
use such content for the purpose of providing other users with access on a regular or large scale
basis or as a means to circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any
jurisdiction, or gives rise to civil liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association
unless explicitly agreed to by Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a
systematic database of Springer Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a
product or service that creates revenue, royalties, rent or income from our content or its inclusion as
part of a paid for service or for other commercial gain. Springer Nature journal content cannot be
used for inter-library loans and librarians may not upload Springer Nature journal content on a large
scale into their, or any other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not
obligated to publish any information or content on this website and may remove it or features or
functionality at our sole discretion, at any time with or without notice. Springer Nature may revoke
this licence to you at any time and remove access to any copies of the Springer Nature journal content
which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or
guarantees to Users, either express or implied with respect to the Springer nature journal content and
all parties disclaim and waive any implied warranties or warranties imposed by law, including
merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published
by Springer Nature that may be licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a
regular basis or in any other manner not expressly permitted by these Terms, please contact Springer
Nature at
onlineservice@springernature.com
Content uploaded by Tomasz Skica
Author content
All content in this area was uploaded by Tomasz Skica on Jul 16, 2018
Content may be subject to copyright.
