Exploring Entrepreneurs’ Motivation: Comparison of Croatia,European Post-socialist and Developed Countries

Abstract

This article explores the entrepreneurial profiles of Croatian entrepreneurs and compares them with the profiles of entrepreneurs of post-socialist and developed European countries. Specifically, logistic regression analysis was used to find the best descriptors of the opportunity and necessity-driven entrepreneurs in Croatia, post-socialist and developed European countries. Results indicate that chances for engaging in opportunity-driven entrepreneurship in Croatia are increasing for men and those who perceive their income level and competencies for running a business as high. The likelihood of engagement in opportunity-driven entrepreneurial activity in the post-socialist context has been related to the age rather than gender and innovations in the product/market matrix or higher export expectations. The developed countries’ datasets reveal that opportunity-driven entrepreneurs have been related to higher expectations of growth through new job openings.

Keywords

opportunity or necessity driven entrepreneurship Croatia European post-socialist countries European developed countries entrepreneurs’profile

According to Cooper and Dunkelberg (1986), Gilad and Levine (1986) or Shapero and Sokol (1982), there are two main triggers of entrepreneurial activity. The ‘push’ trigger is provoked by an unfavourable economic situation of an individual (usually by unemployment, low income or poverty), whereas the ‘pull’ trigger is derived from the desire to seize the recognised entrepreneurial opportunity. More recently, it is common to differentiate between two main types of entrepreneurial activity such as entrepreneurship driven by necessity (push) or by opportunity (pull). Kelley, Bosma and Amoros (2011), Shane (2009), van Praag and van Stel (2013) found there is a significant difference in how these particular types of entrepreneurial activities influence economic growth. Their empirical evidence indicates that opportunity-driven entrepreneurship has positive macroeconomic consequences. It raises productivity, competitiveness and generates more value added. Furthermore, it has a positive impact on economic growth through spill-over effects (Hartog, Van Stel & Storey, 2010). The empirical findings of Acs, Arenius, Hay and Minniti (2005) and Kelley et al. (2011) indicate that in high-income countries, that is, in countries driven by innovation, opportunity entrepreneurship prevails, whereas in low-income countries or countries driven by basic production factors, necessity entrepreneurship prevails.

Opportunity-driven entrepreneurial activities also have numerous positive effects on the microeconomic level. Opportunity entrepreneurs have better financial performances measured by the total income or income per employee (Acs, 2006; Amit & Muller, 1995; Block & Wagner, 2007; Block & Sandner, 2009). They also have a higher likelihood of survival when passing through different phases of business maturation (Verheul, Thurik, Hessels & van der Zwan, 2010). Additionally, they have higher expectations regarding new products, new market creation, exports, new technology adaptation and new job creation (Reynolds, Bygrave, Autio, Cox & Hay, 2002).

Therefore, enhancing the entrepreneurship motivated by opportunity should become a strategic priority of policy makers. The measures for fostering opportunity entrepreneurs will be more successful if a more precise profile of opportunity entrepreneurs is known. However, fostering opportunity-driven entrepreneurship is dependent upon initial position, time path and context (Naude, 2008). An important role in encouraging entrepreneurship is played by the national frame of references: (i) through the development of a culture that cultivates innovation, creativity and responsibility and (ii) through the development of institutions that reduce uncertainty, decrease the costs of business activities and coordinate a dispersion of knowledge and risks among subjects (Boettke & Coyne, 2003). Countries with more stable macroeconomic and political conditions enjoy higher economic growth. They also change their economic structures easier and faster. In these countries, entrepreneurship driven by opportunity dominates.

Central and Eastern European countries, such as Croatia, have been experiencing different initial positions, time paths and contextual frameworks in entrepreneurship when compared to the affluent Western European countries. Their troubles in developing an appropriate entrepreneurial culture, institutional capacity and obeying the rule of law resulted in the deficit of opportunity entrepreneurs.

There is a limited body of evidence exploring the individual level differences in the opportunity or necessity-driven entrepreneur’s profiles (Block & Wagner, 2007; Davidsson & Wiklund, 2001; Kautonen & Palmroos, 2009). Furthermore, there is a lack of empirical research focused on a comparison of differences between the necessity and opportunity motivation of entrepreneurship in the post-socialist or cross-national contexts. The majority of empirical evidence is based on the developed economy context and there is a gap of evidence for transitional economies or less developed countries (Giacomin, Guyot, Janssen & Lohest, 2007; Grilo & Thurik, 2008).

The purpose of this article is to explore how a set of individual characteristics (demographic, psychographic, socio-cultural and growth aspiration) influences the decision to become entrepreneurially active in a case analysis manner. Furthermore, the article focuses on a comparison and explanation of the differences between opportunity- and necessity-driven profiles of entrepreneurially active persons included in the Global Entrepreneurship Monitor (GEM) project aggregated in three subsamples, that is, Croatia, post-socialist and developed countries. Logistic regression analysis is based on the GEM’s Adult Population Survey (APS). As suggested by Isaksson and Ng (2006), a combination of both modes of analysis (a cross-national and a case study analysis) offers the most valuable tool for policy makers as well as for researchers.

The contribution of this article is twofold: (i) it will provide descriptors of desirable types of entrepreneurial activity and (ii) it will identify the main differences between the opportunity- or necessity-driven entrepreneurial profiles in the Croatian, the post-socialist or the developed countries context, in order to enable policy makers to design better policies for fostering specific types of entrepreneurship.

After a literature review, the methodology used in the article is described, followed by results and discussion of the major findings. The article ends with the conclusion.

A Brief History of Research on Factors Influencing the Entrepreneurship Motivation

Empirical studies aiming to analyse the individual factors of entrepreneurial activities are numerous and frequently stress the following factors: (i) demographic (such as gender, age, education, employment and income), (ii) psychographic (role model in family, entrepreneurial experience, knowledge and abilities, propensity to risk, ability to perceive the opportunities, etc.), (iii) organisational and industrial (factors and strategies of operation, ambitions and passion to growth and to be competitive, etc.) and (iv) socio-cultural perceptual (e.g., perception of cultural values, entrepreneurship as a career option or status in the society).

Usually gender has a significant influence on entrepreneurship in general, and on opportunity entrepreneurship in particular. Women are generally less active in entrepreneurship. Additionally, they are engaged in entrepreneurship with a more complex set of motives (for instance, glass-ceiling, the need for balance between professional development and private life, autonomy and the like). They face bigger financial barriers and prejudice. They prefer more entrepreneurial activities in sectors different than men’s (Carter, Gartner, Kelly & Gatewood, 2003; Verheul & Thurik, 2001).

Age is also an important factor of entrepreneurial activity. Levesque and Minniti (2006) describe its connection with entrepreneurship by the inverse U-form distribution. Entrepreneurial activity is larger in the middle age (30–45 years) because a person of that age has acquired and built his/her own competences or formed a network of contacts that ease the overcoming of barriers or ensure control over growth. Block and Sandner (2009) highlight that the likelihood of entrepreneurial engagement due to opportunity decreases as older the person is.

Education has mostly a significant influence on entrepreneurial activity. Grilo and Irigoyen (2006) describe its connection by the classic U-form distribution. Entrepreneurial activity is high by low-educated persons because self-employment is the best alternative to unemployment. Highly educated and competitive persons feel a need for autonomy and capitalisation of opportunities as the way of self-actualisation. These persons also have a higher involvement rate in entrepreneurship. Bergman and Stenberg (2007) find out that opportunity entrepreneurs are more educated.

In comparison with the other group of characteristics, psychographic characteristics are extremely heterogeneous in their forms. Additionally, empirical results show that their direction of influence on entrepreneurial activities is especially inconsistent. For instance, Morales-Gualdron and Roig (2005) and Wagner (2005) find out that perception of self-efficiency or entrepreneurial experiences positively influence entrepreneurial activity. They also find that a fear of failure can become an important constraint for entrepreneurial engagement; hence, it negatively influences entrepreneurial activity. On the other hand, Lee and Peterson (2000) conclude that a fear of failure and propensity to avoid risk can even have a positive influence on entrepreneurial activity. It seems that psychographic characteristics reflect to a large degree not only the influence of individual demographic factors, but also external factors, especially organisational, industrial and socio-cultural ones.

Organisational and industrial factors include the structure of resources and micro-factors of success. Reynolds et al. (2002) stress that opportunity entrepreneurs have more chances for success because they start their venture with a more innovative technology, more knowledge on industry and markets as well as with more ambitious plans of venture growth in comparison with necessity entrepreneurs.

As the basis of individual attitudes, cultural programming has different influences on entrepreneurial activities. Uhlaner and Thurik (2007) detect that low propensity to material values is connected with a lower rate of entrepreneurial engagement. High propensity to collectivism and acceptance of high status or position power of an individual and uncertainty avoidance showed to be important determinants of innovation capacity on the macroeconomic level (Shane, 1992). Institutional, political or contextual frames of reference have an interesting influence on individual characteristics of entrepreneurial behaviour. Acs, Desa and Klapper, (2008), Ardagna and Lusardi (2008), Estrin, Korosteleva and Mickiewicz (2011) as well as Verheul et al. (2010), confirm the influence of a legal and institutional (policy) framework on transformation from early to more mature phases of entrepreneurial activities or on motivation orientation. Runst (2010) finds out that the institutional context of transition countries is more conductive to engage in entrepreneurship driven by necessity.

Empirical findings indicate that socio-economic and demographic factors have a more discriminatory role than the psychographic or perceptual ones (Davidsson & Wiklund, 2001), and that the influence of a different group of characteristics is nested, intertwined, cumulative and hard to isolate (for example, aging leads to multiplication of experience and knowledge). The literature review shows that the same factors can have a different direction of influence on entrepreneurship driven by necessity or opportunity in different contextual frames (Block & Wagner, 2007; Kautonen & Palmroos, 2009).

There is still a controversy in the generalisation of the descriptors of different motivational types of entrepreneurship activity. Previous empirical studies on individual profiles of opportunity or necessity entrepreneurs have been focused mostly on developed countries. A few previous studies (e.g., Runst, 2010; Verheul et al., 2010) show that entrepreneurship driven by necessity is more likely to appear in the context of transition and post-socialist countries compared, for instance, with the Scandinavian, Anglo-Saxon, South-East or Central-East European context.

Bearing in mind the findings of the literature review, particularly regarding the insufficiency of knowledge on factors influencing the engagement in entrepreneurial activity driven by opportunity or necessity in different socio-economic and institutional contexts, this article presumes that entrepreneurship motivation is related to a set of demographic, psychographic and perceptive (socio-cultural, growth expectations and organisation/industry related) characteristics. In addition, two research hypotheses are stated:

H₁: There are differences between the opportunity- and necessity-driven entrepreneur profiles;

H₂: Croatian entrepreneur profile shows different characteristics compared with the post-socialist and the developed European countries.

Data and Method

The GEM is an international research project, which has collected data on entrepreneurship activities from 1997. Based on a survey of adults (APS) of working age population (18–64 years) being conducted annually, it measures the individual’s attitudes towards entrepreneurship, his/her motivation, expectations regarding starting a new business or managing young entrepreneurial ventures. This article takes the GEM European participating countries in 2007 as the referent basis for a comparison with the Croatian entrepreneur profile. The year 2007 is the most recent publicly accessible database open to researchers.

Major indicators that describe entrepreneurship activity across different nations are total entrepreneurship activity (TEA index), the opportunity-driven (TEAOPP) and the necessity-driven new entrepreneurial activity (TEANEC). These indicators are calculated from the data collected annually through the adult population survey (APS). It is a standardised questionnaire conducted by the independent vendor in each participating country, strictly monitored to follow a uniform set of rules to ensure international comparability of the data. TEA indicates the percentage of the adult population engaged in the process of start-up or owning/managing operations in new firms not older than 42 months. TEAOPP and TEANEC indicate the percentage of the adult population motivated by either opportunity or necessity.

European participants in the GEM research (in addition to Croatia) were classified into two aggregated datasets. The group of post-socialist countries comprises the GEM individual country level APS datasets of six countries: Russia, Hungary, Romania, Latvia, Serbia and Slovenia. The group of developed European countries aggregates the GEM APS datasets for 14 countries: Greece, the Netherlands, Belgium, France, Spain, Italy, Sweden, Austria, Great Britain, Denmark, Norway, Portugal, Ireland and Finland. In addition to these datasets, the article also explores the Croatia’s individual country APS data for 2007. Croatia is a post-socialist country in transition between efficiency and innovation-driven economy. Table 1 shows sample sizes and the number of entrepreneurially engaged persons in a particular sample.

The APS tracks opportunity and necessity motivated entrepreneurial engagement. In this article, TEAOPP and TEANEC are dependent variables. TEAOPP is an indicator variable equal to one if individuals are starting a new business or are owners and managers of a young firm to take advantage of a business opportunity; otherwise, it is equal to zero. The same holds for TEANEC. It should be stressed that, for instance, ‘zero’ in TEANEC cannot be interpreted as entrepreneurial activity driven by opportunity.

In order to test the relationship between the dependent and the independent variables, from the GEM APS 2007 we selected variables based on the prior GEM-based research. A more detailed description of variables is given in the Table 2.

One should bear in mind that: (i) the choice of independent variables followed the theoretical and empirical findings; however, it was constrained by the available GEM individual country level APS dataset; (ii) certain perceptive variables are subject to autosuggestions and self-assessment; therefore, the answers could be over- or underestimated.

Since the dependent variables, TEANEC and TEAOPP are binary categorical variables, logistic regression analysis is chosen for describing and testing hypotheses on relationships between them and the set of categorical or continuous predictor (independent and explanatory) variables. Logistic regression estimates the probability of a certain event occurring based on a set of independent variables. Detailed technical descriptions of the logistic regression can be found in Hosmer and Lemeshow (2000) or in Menard (2009).

Table 1.

Sample Sizes and Subsamples of Adults Who are Entrepreneurially Active due to Opportunity, Necessity or Other Motive

	Croatia	European GEM Post-socialist Countries	European GEM Developed Countries
Sample size	2,000	12,705	99,400
Number of persons (age: 18–64 years) engaged in new entrepreneurial activity (TEA)	136	564	5,580
Number of persons (age: 18–64 years) engaged in entrepreneurial activity due to opportunity TEAOPP	83	392	4,354
% TEAOPP/TEA	61	69.5	78
Number of persons (age: 18–64 years) engaged in entrepreneurial activity due to necessity TEANEC	48	137	685
% TEAOPP/TEA	35	24.3	12.3
Number of persons engaged (age: 18–64 years) in TEA for other motives (TEAOM)	5	35	541
% TEAOM/TEA	4.0	6.2	9.7

Source: GEM APS—Individual country level datasets 2007.

Table 2.

Descriptive Statistics

Name	Description and Scales	Mean	Std. Dev
Dependent variables
TEANEC	1 if individuals are involved in necessity early-stage entrepreneurial activity, 0 otherwise	0.02	0.15
TEAOPP	1 if individuals are involved in opportunity early-stage entrepreneurial activity, 0 otherwise	0.04	0.20
Independent variables
Demographic variables
GENDER	1 if male, 0 otherwise	1.52	0.50
AGE	Age of the individual at the time of the interview, 7 categories: 1 if the individual is younger than 17, 2 for those aged 18–24 years, 3 aged 25–34 years, 4 aged 35–44 years, 5 aged 45–54 years, 6 aged 55–64 years and 7 aged 65–98 years	4.48	1.80
WORK	Work status, 3 categories: 1 if the individual works, 2 if he/she does not work, 3 if he/she is retired or a student	1.92	0.91
INCOME	Income recoded into thirds: 1 if the person is in the lowest, 2 in the middle, 3 in the upper 33rd income percentile of their country’s income distribution	1.78	0.77
EDUCATION	Educational attainment, 4 categories: 1 if the individual has some secondary school, 2 secondary degree, 3 postsecondary degree and 4 university degree	1.68	1.11
Psychographic variables
DISCENT	1 if the person sold, shut down, discontinued or quit a business he/she owned and managed in the past 12 months, 0 otherwise	0.03	0.16
KNOWENT	1 if the person knows someone who has started a business in the past two years, 0 otherwise	0.58	0.49
OPPORT	1 if the person thinks that there will be good opportunities for starting a business in the next six months, 0 otherwise	0.48	0.50
SUSKILL	1 if the person thinks he/she has the knowledge, skills and experience to start a new business, 0 otherwise	0.62	0.49
FEARFAIL	1 if the person’s fear of failing can prevent him/her from starting a new business, 0 otherwise	0.30	0.46
Perceptual variables—socio-cultural norms and values
EQUALINC	1 if the person thinks that most people would prefer that everyone had a similar standard of living in the country, 0 otherwise	0.80	0.40
CAREER	1 if the person thinks that most people consider starting a new business a desirable career choice in the country, 0 otherwise	0.71	0.45
STATUS	1 if the person thinks that those successful at starting a new business have a high level of status and respect in the country, 0 otherwise	0.51	0.50
MEDIA	1 if the person thinks that someone can often see stories in the public media about successful new businesses, 0 otherwise	0.63	0.48
Perceptual variables—industry/organisational and growth aspiration
COMPET	Number of businesses that offer the same products, 3 categories: 1 many, 2 few and 3 none	1.70	0.65
TECHNO	Number of years for which the technologies or procedures required for this product or service have been available, 3 categories: 1 latest technology (newer than one year), 2 new technology (1 or 5 years), 3 no new technology (more than 5 years)	2.24	0.82
MODEL	New product market combination, 2 categories: 0 (no indication), 1 (indication)	0.22	0.42
JOBS	Expected number of jobs, 4 categories: 1 (no jobs), 2 (1–5 jobs), 3 (6–19 jobs) and 4 (20+ jobs)	2.63	0.81
EXPORTS	Export intensity, 5 categories: 1 (76–100%), 2 (26–75%,) 3 (11–25%), 4 (1–10%,) 5 (none)	3.38	1.30
INDUSTRY	Type of industry, 4 categories: 1 (extractive), 2 (transforming), 3 (business services) and 4 (consumer-oriented)	2.89	1.01

Source: GEM Adult Population Survey—Individual country level dataset 2007.

We estimate the following logit model: ₁(1)₂₃₄₅₆₇₈₉₁₀₁₁₁₂₁₃₁₄₁₅₁₆₁₇₁₈₁₉₂₀

Where a, β₁, …, β₂₀ are parameters that should be estimated for Croatia, the post-socialist and the developed European countries. The outcome of interest (y = 1) has already been noticed—engagement in entrepreneurial activity due to necessity or opportunity (in our case: TEAOPP = 1, TEANEC = 1). Logit analysis is conducted by using the logistic procedure in Eviews 5 and SPSS 18 in Windows 2000 environment.

Since the psychographic variables are nominal, they were transformed into binary ones; positive answers were categorised as the reference category (as ‘1’), negative answers as ‘0’, while all other answers (not known, missing data, cannot be categorised), that is, observations with such answers were dropped from the analysis. Observations with the same answers by other nominal predictors were also dropped. With respect to the variable gender, that is also nominal, women were set to be the reference category.

The diagnosis of logit model (1) was based on: (i) multicollinearity detection, (ii) overall model evaluation and (iii) checking for statistical significance of individual predictors. Multicollinearity was tested in SPSS. Tolerance and variance inflation factors (VIFs) were used for that purpose. Evaluation of the overall model was done by comparing it with the null, that is, with the intercept-only model. The likelihood ratio (LR) test was used to find out whether the model provides a better fit to data. The Hosmer–Lerneshow test (H–L test) and McFadden R²were used to assess how well a logistic model fits a set of observations. Statistical significance of individual regression coefficients was tested by using the Wald χ² statistic.

Results

The results of binomial logistic regression analysis for three different socio-economic and institutional contexts—Croatian, post-socialist and developed European countries for 2007 are shown in Table 3.

Table 3.

Logit Analysis of Engagement into Entrepreneurial Activities because of Opportunity or Necessity

	Croatia		European Post-socialist Countries		European Developed Countries
Dependent Variable	TEAOPP=1	TEANEC=1	TEAOPP=1	TEANEC=1	TEAOPP=1	TEANEC=1
Demographic variables
GENDER	–1.239(–2.94)[0.290]	1.443(2.34)[4.235]
AGE			–0.303(–2.97)[0.739]	0.417(3.47)[1.518]	–0.139(–4.58)[0.871]
EDUCATION				–0.402(–2.57)[0.669]
INCOME	1.502(3.19)[4.491]	–1.261(–2.74)[0.284]	0.975(6.50)[2.652]	–1.002(–2.57)[0.367]	0.260(4.87) [1.297]	–0.415(–5.94)[0.660]
Psyhographic variables
OPPORT	1.574(2.39)[4.827]	–1.581(–2.28)[0.206]			0.337(3.79)[1.400]	–0.543(–4.97)[0.581]
SUSKILL	1.515(1.98)[4.548]	–1.784(–2.25)[0.168]	0.776(2.39)[2.174]	–0.932(–2.70)[0.394]	0.547(4.49)[1.728]	–0.380(–2.47)[0.678]
FEARFAIL		1.500(2.16)[4.482]				0.251(2.11)[1.286]
Perceptual variables—industry, organisational and growth aspiration
COMPETITION	1079(2.46)[2.941]	–1.229(–2.63)[0.117]				–0.248(–2.94)[0.781]
MODEL			0.832(2.27)[2.298]
JOBS					0.254(5.17)[1.290]	–0.218(–3.21)[0.804]
EXPORTS			–0.202(–2.37)[0.817]	0.269(2.80)[1.309]
Perceptual variables—socio-cultural context
EQUALINC	2.073(2.78)[7.947]	–1.645(–2.23)[0.193]
MEDIA	–1.901(–2.76)[0.149]	2.186(2.99)[8.897]			0.226(2.56)[1.254]	–0.252(–2.31)[0.777]
Constant	–5.562(–2.94)[0.001]	5.634(2.24)[279.903]				0.476(1.75) [1.608]
LR stat(df; prob.)H–L stat(df; prob.)McFadden R²	41.517(7; 0.00)6.335(8; 0.610)0.32	43.609(8; 0.00)9.624 (8;0.292)0.34	134.93(5; 0.00)3.609(8; 0.891)0.421*	182.979(5; 0.0)7.313(8; 0.503)0.536*	1314.075(6; 0.00)6.019(8; 0.645)0.438*	128.850(7; 0.00)5.305(8; 0.725)0.05

Notes: z-statistic is given in (parentheses), while the odds ratio is given in [square brackets].

df denotes degree of freedom.

* Model hi-quadrate statistics (df, probability).

** Nagelkerke R²

Label Description

OPPORT 1 if the person thinks that there will be good opportunities for starting a business in the next six months, 0 otherwise

SUSKILL 1 if the person thinks he/she has the knowledge, skills, and experience to start a new business, 0 otherwise

FEARFAIL 1 if the person’s fear of failing can prevent him/her from starting a new business, 0 otherwise

COMPETITION Number of businesses offer the same products, 3 categories: 1 many, 2 few, 3 none

MODEL New product market combination, 2 categories: 0 no indication, 1 indication

JOBS Expected number of jobs, 4 categories: 1 no jobs, 2 1–5 jobs, 3 6–19 jobs, 4 20+ jobs

EXPORTS Export intensity, 5 categories: 1 (76–100%), 2 (26–75%), 3 (11–25%), 4 (1–10%), 5 – none

EQUALINC 1 if the person thinks that most people would prefer that everyone had a similar standard of living in the country, 0 otherwise

MEDIA 1 if the person thinks that someone can often see stories in the public media about successful new businesses, 0 otherwise

In Croatia, the chances for engaging in entrepreneurship motivated by opportunity are increasing for men and those who perceive their income level and competencies for running a business as high. Additionally, persons able to perceive business opportunities in their environment are more likely to engage in opportunity entrepreneurship, as well as those who perceive that the majority prefers a similar standard of living. On the other hand, media have a negative impact on the likelihood of engaging in entrepreneurship motivated by opportunity. The same variables are significant in explaining the likelihood for engaging in necessity-driven entrepreneurship, but their impact is opposite. Since this also holds for other samples in general, hypothesis H₁ is confirmed: There are significant differences in demographic, psychographic or organisational characteristics between the opportunity- and necessity-driven entrepreneurs.

The analysis confirms hypothesis H₂ as well. There are differences in the profile of opportunity-driven entrepreneurially active persons in different contexts, particularly in Croatia compared with the other socio-economic and institutional context. The likelihood of engagement in opportunity-driven entrepreneurial activity in the post-socialist context has been related to the age rather than gender, and innovations in the product/market matrix or higher export expectation. The developed countries datasets reveal that opportunity entrepreneurs have been related to higher expectations of growth through new jobs openings. These findings should highlight the perils of the common policy practices that are usually non-selectively enforced and copied irrespectively of the context where they will be implemented.

Discussion

The number of statistically significant variables that profile opportunity- and necessity-driven entrepreneurs in different contexts is relatively small. Besides, the variables predicting engagement in entrepreneurial activity may be more or less the same; however, their impact is opposite. This should be kept in mind when designing policies where measures beneficial to necessity entrepreneurs might be hurtful for opportunity entrepreneurs. The usual ‘one size fits all’ approach might be less beneficial than expected.

The Croatian sample shows several distinctive features. First, women are still much more vulnerable in the labour market and less prevalent in opportunity-based entrepreneurship. Second, the perception of egalitarian values seems to have a positive impact on chances to start entrepreneurial activity due to opportunity. Third, media coverage of entrepreneurship does not increase the likelihood of a person to start opportunity-based entrepreneurial activity. The results point out that the Croatian opportunity entrepreneurs, who are more educated and perceive their income as higher, are more inclined to accept the notion that entrepreneurial activity should be socially responsible and not run for the sake of private benefits only. It seems that socialist egalitarianism (equality of outcome for all) has been slowly turning into liberal egalitarianism (equality of opportunity for all). When it comes to necessity-based entrepreneurs, achievement of broader economic and social interest is not evident in their behaviour, and survival is the main trigger of their entrepreneurial activity. The results also indicate that media coverage has a significant yet negative impact on opportunity entrepreneurs. This might be explained by the types of usual ‘success’ stories covered by media. Frequently, media present success stories about entrepreneurs who are later charged with fraud or misconduct. Opportunity entrepreneurs probably do not perceive those stories as good role models or best practices they can follow. On the other hand, Levie, Hart and Karim (2011) and Hang and van Weezel (2007) indicate that generally media have a positive and significant role in creating the image of entrepreneurs, role models or even entrepreneurial attitudes. Therefore, media coverage is a regular part of the system measures for enhancing entrepreneurship all over the world. However, finding role models that will have a positive influence on others is not an easy task, whereas presenting unaccountable role models might have an obstructive impact.

In a nutshell, the set of uniform measures could have an opposite influence on different types of motivation for starting an entrepreneurial venture. Some programs can be beneficial for necessity entrepreneurs; however, they can be hurtful for those who need to decide whether to seize their own opportunity or not.

A comparison of opportunity-based entrepreneurship profiles in different contexts offers several insights. Compared with post-socialist and developed European countries, where age is a statistically significant demographic variable influencing entrepreneurial activity, gender has such role in Croatia. The likelihood of involvement in entrepreneurship driven by opportunity is higher for men. Empirical results indicate that the income level and the perception referring to having appropriate knowledge and skills have a statistically significant impact on starting a business regardless of samples. A higher income level and the perception of knowledge increase the likelihood of involvement into entrepreneurial activity in order to seize the business opportunity. However, these variables decrease the likelihood of involvement in necessity entrepreneurship by opening new horizons and perspectives of seeing and evaluating the business reality.

The ability to recognise a business opportunity increases the likelihood of involvement in entrepreneurial activities driven by opportunity in Croatia and in developed countries; it simultaneously decreases the likelihood of involvement in entrepreneurial activity driven by necessity.

The role of media in promoting entrepreneurship is important for Croatia and the European countries. Its role is positive in the European country sample, meaning that media increases the likelihood of involvement in entrepreneurial activity because of opportunity, whereas it acts just the opposite in Croatia. In relation to egalitarianism, there is another difference between Croatia and other contexts. This variable is statistically significant only for Croatia; it has a positive impact on starting a business to seize an opportunity.

Working status, social-networking, status of entrepreneurship in a society and building a career in entrepreneurship followed by technological innovativeness do not significantly influence the likelihood of involvement in entrepreneurial activities because of either necessity or opportunity.

While at least one variable that influences the innovative potential of entrepreneurs has had a statistically significant impact on start-up motivation, neither has had such role in Croatia. The important finding is the indication that regardless of motivational triggers entrepreneurship in Croatia has not been influenced by growth aspiration variables (such as export expansions, innovativeness of the product/market, number of new jobs generated, etc). Other research indicates that opportunity entrepreneurship has higher aspirations for new jobs, innovativeness, market expansions or growth expectations. Croatia seems to encourage a general mainstream entrepreneurial activity, where opportunity entrepreneurs are niche-oriented, a kind of ‘fly below the radar’—unnoticed from the taxation or other economic authorities—and have an incremental impact on the economy as a whole. In order to enhance the overall impact of entrepreneurship on economic growth, first of all, Croatia needs more stable, consistent and better ‘rule of law’, a more efficient state institution and better administrative capabilities of entrepreneurship infrastructure. It also needs entrepreneurship-specific education as it reduces the odds of failure for entrepreneurs. Additionally, since it demands more high-growth aspiration entrepreneurs, policies aimed at stimulating this kind of entrepreneurs should not be similar to those encouraging necessity entrepreneurs.

There are some limitations of these findings. Using the GEM dataset as an available pool of internationally comparable data has its shortcomings pertinent to every longitudinal research of such scope: the survey method is subject to conceptual or perceptual framing, there is a difference in the availability of data for post-socialist and developed countries, categorisation of opportunity or necessity motivation is highly generalised, etc. However, we believe that even such evidence can be useful in providing particularly policy makers with more in-depth descriptors of opportunity-driven entrepreneurship, since it is more desirable and has a more positive impact on economic or social welfare.

Conclusion

Economic consequences of entrepreneurship driven by opportunity or necessity differ significantly, whereas entrepreneurship motivated by opportunity generates higher productivity, competitiveness and value-added. Therefore, there is a huge interest of policy makers to multiply them. Regardless of motivation, factors influencing entrepreneurial activities are numerous, heterogeneous, mutually intertwined and dependent. Therefore, it is challenging to find out which of them has a significant impact on entrepreneurial activity. In order to identify them, this article employed logistic regression analysis testing the direction and importance of the relationship between a binary dependent variable and the whole set of independent variables. The data were collected from the GEM database, and they were classified into demographic, psychographic and perceptual variables (socio-cultural, organisational-industry and growth aspiration).

The results for Croatia show that only a few variables are significant. Among demographic variables, gender and income are statistically significant, whereas perception of competences, fear of failure, competition, egalitarianism and media coverage are statistically significant among psychographic, industrial or socio-cultural variables. The likelihood of involvement in opportunity entrepreneurship is increasing when it comes to men that have a higher income. It also increases with the ability to recognise business opportunities and presumption into own knowledge, which at the same time makes transition from necessity to opportunity entrepreneurs easier. This confirms the importance of multiplication and making heterogeneous external finance sources as well as entrepreneurship education. Furthermore, the Croatian profile of opportunity entrepreneurs shows that the likelihood of involvement in opportunity entrepreneurship is increasing if a smaller number of competitors offer similar products. These findings imply niche orientation of Croatian opportunity entrepreneurs.

Although the empirical results of this study are consistent with the others, certain divergence exists when it comes to the perception of the role of media and egalitarianism. Whereas the results of other studies indicate a positive role media could have in developing an image of entrepreneurs, friendly and innovative entrepreneurial culture and the whole system of entrepreneurship fostering, our findings do not confirm such role. A plausible explanation for this can be found in the congested media space in Croatia with the examples of entrepreneurs that grew very fast and became successful in a suspicious manner, and whose recipes for success are non-transparent or non-imitative, particularly to opportunity entrepreneurs. Regarding egalitarianism, it seems that the period of self-managed socialism in Croatia, which is its source, left a mark on opportunity entrepreneurs and their expression on entrepreneurship as the driver of positive social changes for whole population when it comes to necessity entrepreneurship, a broader economic and social interest does not come to the fore and entrepreneurs of this kind are motivated by the need of survival.

Although the results of other studies indicate the importance of variables that determine the innovation potential and growth aspiration, particularly for opportunity entrepreneurs, in Croatia, neither variable has a statistically important influence on entrepreneurs regardless of their motivation. One can look for an explanation for that in factors out of the individual frame of references, particularly in those that determine the quality of the legal and institutional framework.

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