The Influence of the Ecosystem on the Motivation of Social Entrepreneurs

Abstract

Social entrepreneurs have particular characteristics that differentiate them from commercial entrepreneurs, but research on this differential behavior is still a field in which many questions need to be explored. Specifically, a factor of special relevance is the ecosystem where social entrepreneurial activity takes place. The aim of this study is to analyze how the ecosystem affects the motivation of social entrepreneurs compared to commercial entrepreneurs. This general objective is divided into two specific objectives. First, we analyze how the ecosystem influences the probability of being a social entrepreneur, considering both factors of the entrepreneurial environment and economic and financial factors. Second, we analyze whether the effect of the entrepreneurial ecosystem is determined by the level of development of the country where the activity takes place. The results show that the entrepreneurial ecosystem (the entrepreneurial, financial, and institutional environment) determines the motivation of social entrepreneurs in a different way compared to commercial entrepreneurs. In addition, we find that this influence is different according to the level of development of countries.

1. Introduction

The current trend is progressively evolving toward a society in which social and environmental problems take on greater importance: there is a growing wealth inequality, a movement toward corporate social responsibility, failures remain at both the market and institutional level, and great advances are taking place in the field of technology and responsibility-sharing [1]. Social Entrepreneurship arises as a possible solution to these obstacles, whose aim is to demolish frontiers by building alternative economic models, in which the social end is considered, as well as the traditional economic goals of commercial entrepreneurs.

This means that the activities and characteristics of the social entrepreneur have their own peculiarities, but research on this differential motivation continues to be a field to be explored, since many questions still have to be answered [2], being especially necessary in the case of quantitative research. However, not only does the main objective of the social entrepreneur imply differences compared to the commercial entrepreneur, but the reasons for this differential motivation can be affected significantly by the entrepreneurial ecosystem, which is understood as the environmental conditions where entrepreneurs develop their activity [3].

The aim of this paper is to analyze the influence of the ecosystem on the social entrepreneur’s motivation, identifying the most significant obstacles and stimuli that motivate the entrepreneur to create a company, differentiating the effect of these factors on the motivation of the social and commercial entrepreneur. The effect of the ecosystem on social entrepreneurial motivation is analyzed from two complementary points of view. The first one analyzes the influence of the ecosystem (entrepreneurial environment and financial and institutional environment), while the second one includes the moderating effect of the country’s level of economic development, in order to analyze the effect that the environment has on the influence of relevant factors, including the ecosystem itself.

In order to carry out the objective of the empirical study, two different analyses have been carried out, both including internal and external factors. On the one hand, the first one allows differentiating the general determining factors of entrepreneurship and analyzing the influence that these have on the social and commercial entrepreneur. On the other hand, the second analysis differentiates the first model into two categories based on the country’s level of development (non-innovation driven and innovation-driven countries), in order to analyze the effect that the development of each country has on internal and external factors that explain social entrepreneurs’ motivation.

The main contribution of this paper is to provide quantitative evidence on the effect that the ecosystem has on the social entrepreneur’s motivation and how that effect is conditioned by the level of development of the country. In this way, this study complements the quantitative research carried out on Social Entrepreneurship that has been arising in recent years, but that is still scarce compared to the large number of studies mainly based on theoretical reviews.

The study is carried out by applying multinomial logistic regression analysis, using the Global Entrepreneurship Monitor’s international database as the main source of information. In addition, the databases Doing Business and Global Financial Development are used, complementing the study with information related to the country’s financial and institutional environment. The results obtained show that both internal and external factors are decisive in the process of creating a company in general, but different effects between commercial and social entrepreneurship can be highlighted. Moreover, considering the development of the country, the effect of these determining factors is modified, both for internal and external characteristics.

The rest of the paper has been structured in four sections. Firstly, a conceptual framework is presented in order to allow the Social Entrepreneurship term to be contextualized, as well as a review of the current literature on the determining factors of its motivation. Secondly, the methodology is described, differentiating the different data sources, the variables used, and the empirical model that enables obtaining the results of the analysis. Thirdly, the results of the general analysis and the analysis differentiated by country category are presented, showing the main differences found in both approaches. Finally, the main conclusions of this research are discussed.

2. Theoretical Framework

Although the current literature constantly raises several definitions of this concept, there is a clear controversy regarding its meaning [4,5,6,7]. However, in the general definition proposed by [8] in the Global Entrepreneurship Monitor (GEM) Social Entrepreneurship Report, the fundamental approaches of Social Entrepreneurship are grouped into “people who are currently starting or running any type of activity, organization or initiative that has a particularly social, environmental or community objective”. In a complementary way, the GEM proposes a narrower definition of Social Entrepreneurship, which incorporates two peculiarities: “that this activity, organization or initiative (i) prioritizes social and environmental value over financial value; and (ii) it operates in the market producing goods and services”.

Despite this lack of agreement on conceptual delimitations, there are some coinciding basic assumptions in most definitions of Social Entrepreneurship [9]. The double dimension, based on the social mission and sustainability, constitutes the main differentiating element of this type of entrepreneurship. It is worth noting the importance of this economic value being sustainable over time—that is, that wealth is generated for society through permanent changes.

As for some of the elements that characterize Social Entrepreneurship, these are factors equally present in the traditional concept of entrepreneurship, such as proactivity, innovation, or the place of development of the company, being possible to find it within an already established business (intrapreneurship) or in the creation of a new company.

The greatest challenge in defining Social Entrepreneurship is to establish the limits between what is considered social entrepreneurship compared to commercial entrepreneurship. If the differentiation between the two types of entrepreneurship is addressed from the perspective of the aim pursued [10,11], the great difference is that business entrepreneurs are focused on an economic approach, while social entrepreneurs are focused on solving a social problem: “These individuals have no desire to enrich themselves, but to make others rich in spirit or fulfillment” [12].

During the process of creating either a commercial or social enterprise, a set of factors are involved, which are decisive for the entrepreneur to convert a business idea into a real enterprise. We can group these determinants into internal or external factors.

Regarding internal determinants, [13] states that knowing an entrepreneurial network, the perception of opportunities in the environment, having the skills and abilities to start a business, and fear of failure are values and attitudes that mark the difference between the behavior of the social and commercial entrepreneur. In addition, the decision to start a business is conditioned by the entrepreneur’s personal characteristics, such as gender, age, or education.

Authors such as [14] state that individuals who in their lives have been able to observe and learn attitudes typical of an entrepreneur, such as the search for alternative paths, taking risks, or taking advantage of opportunities will be more likely to start entrepreneurial activities. The same positive relationship can be expected for social entrepreneurs, who, in addition to sharing the above reasons with the commercial entrepreneur, include contact with examples of altruistic people as a motivation to start a business [15].

Furthermore, it could be deduced that the number of opportunities in the environment leads to a greater perception of these by individuals and, therefore, a greater probability of starting a business. However, where commercial entrepreneurs detect problems, social entrepreneurs observe opportunities. Therefore, considering the numerous problems in society today, a greater source of opportunities for social entrepreneurs can be expected [16]. However, the latter have difficulties associating these problems with an opportunity (proactivity of the entrepreneur) and, in turn, to deal with their complexity with an innovative idea and with sufficient entrepreneurial autonomy [17,18].

On the other hand, having entrepreneurial skills and abilities enables the entrepreneur to deal with the inherent risks of entrepreneurship and to run the business successfully, thus increasing the probability of starting a business initiative. If we want to understand the figure of the social entrepreneur, we have to pay attention to his/her abilities and the way the entrepreneur perceives he/she can take advantage of these abilities. [19,20]. The fact of solving a multidimensional problem to help others makes the individual perceive himself as more valuable and, therefore, the effect of this determinant is considered to increase for the social entrepreneur.

Similarly, entrepreneurship involves taking a risk that could influence the individual when creating a company. The factors that influence an individual’s fear of failure are mainly based on social stigma, economic loss, feeling of shame, or social exclusion [21]. In the case of the social entrepreneur, on the one hand, the aforementioned personal risk is distinguished and, on the other hand, there is the financial risk, which leads the social entrepreneur to have fewer levels of fear of failure thanks to financing, in many cases, with resources that are not his own [18].

Together with the previous values and attitudes, there are intrinsic characteristics of the entrepreneur that also condition the decision to start a business. For example, in entrepreneurship, the male gender is the predominant one [22]. However, [23] establish a greater identification of women with social issues and the environment and, therefore, an increase of this gender in the case of Social Entrepreneurship.

The entrepreneur’s age is another determinant, but the evidence found so far is not yet conclusive. On the one hand, and the same as in the case of commercial entrepreneurs [24,25], empirical evidence indicates that younger people may be more inclined to participate in Social Entrepreneurship [13,26,27,28]. A predominant presence of young people in entrepreneurship is supported by factors such as a lower perception of fear of failure, a low family burden, or greater creativity and sensitivity to technological changes. However, younger people also have a reduced professional experience or education, a greater restriction to obtaining financing, or a scarce network of contacts, which can lead to older individuals being the ones more oriented toward entrepreneurship. In the case of the social entrepreneur, we also find inconclusive evidence. On the one hand, we have proposals such as [29] that find that young people value non-materialistic goals more and, therefore, opt for the creation of social enterprises. On the other hand, we can find that an entrepreneur is more likely to be social in the intermediate years, but not in the youngest and oldest age groups [30].

Furthermore, the entrepreneur is not only influenced by gender or age, but education also determines the decision to start a business. Specifically, [31] states that the individual who has a higher educational level has a greater probability of starting a business, since higher education implies greater training and confidence to create a business and less difficulty to access resources, which among other factors, encourage entrepreneurship. Regarding social entrepreneurship, there is a similar effect on the educational level [30,32,33]. The effect can even be intensified, as the social entrepreneur faces more insecure environments with possible financial problems that a higher education could help to cope with successfully.

However, beyond the entrepreneur’s individual factors, the entrepreneurial ecosystem plays a very important role [34,35,36,37]. This ecosystem considers external determinants, such as the economic, political, socio-cultural, or legal environment that surrounds the entrepreneur [38,39,40]. Based on this context, a closer and more specific environment of the entrepreneur could be distinguished from a more general environment [41].

Specifically, [42] find that a favorable regulatory, normative, and cultural dimension increases the probability of being an entrepreneur. The regulatory dimension consists of laws, regulations, and government policies that support new enterprises [43]. The normative dimension measures the degree to which residents of a country admire business activity and value creative and innovative thinking [43]. Culture is an important reflection of a country’s informal institutions [44] and shapes the context in which entrepreneurship takes place [45].

However, studies on social entrepreneurship and economic development of the country, such as [46], show that the general environment plays a relevant role. Their results show that the country’s level of development could influence the process of creating a company and generate differences between commercial and social entrepreneurship. In this regard, [29] also reveal that social entrepreneurship is a phenomenon strongly driven by the wealth level of a country and suggest an inverted U shape, because although the demand for social entrepreneurship activities may be lower in the wealthiest countries, the prevalence of social entrepreneurship is positively affected by the level of economic development. This influence led [47] to establish that studies that obtain results within the context of developed countries may be inappropriate and therefore ineffective in the context of developing countries.

3. Empirical Analysis

3.1. Sample

The main source of data used for the empirical analysis is based on the Global Entrepreneurship Monitor (GEM), which is an international observatory of entrepreneurship experts that annually analyzes the entrepreneurial phenomenon worldwide. The use of a homogeneous methodology at a global level enables comparing the entrepreneurial situation between different countries. Specifically, two information sources from the GEM project are used: The Adult Population Survey (APS) and the National Expert Survey (NES). The information corresponds to the surveys of 2015, since a specific section related to social entrepreneurship was included that year.

In addition, the Doing Business (DB) and Global Financial Development (GFD) databases have been used, which provide information on the regulations for doing business and different characteristics of the financial system, respectively.

The composition of the sample is shown in

Table 1. Sample composition.

Country’s Development	Non-Innovation Driven	%	Innovation-Driven	%	Total
No. of countries	35	62.5%	21	37.5%	56
Non-entrepreneurs	60,908	53.8%	52,268	46.2%	113,176
Commercial entrepreneurs	11,630	75.8%	3716	24.2%	15,346
Social entrepreneurs	6423	66.6%	3226	33.4%	9649
Total	78,961	57.1%	59,210	42.9%	138,171

Source: Global Entrepreneurship Monitor (GEM) data (2015).

, which shows the number of countries included in the analysis according to the level of development, as well as the number of non-entrepreneurial individuals, commercial entrepreneurs, and social entrepreneurs.

Regarding the number of countries, the classification of the 56 countries included is established according to the Global Competitiveness Index (GCI) of the World Economic Forum (WEF), distinguishing two different groups of countries. On the one hand, we have developed countries with economies based on innovation (innovation-driven economies), and on the other hand, we have countries with lower levels of development, which have economies based on factors or efficiency (factor/efficiency-driven economies).

Regarding the categorization of entrepreneurs, following the definition of GEM, we identify as a social entrepreneur an individual who is initiating or running any kind of activity, organization, or initiative that has a social, environmental, or community objective [8]. We identify the entrepreneurial population that is not considered social entrepreneurs as commercial entrepreneurs.

The sample contains information from 56 different countries, 21 innovation-driven economies representing 59,210 individuals (almost 43% of the total sample). However, if we look at the entrepreneurial population, that proportion is reduced, with commercial entrepreneurs being less than 25% of the total. In the case of social entrepreneurs, that percentage rises to 33.4%, but they are still far from the 46.2% that represents the non-entrepreneurial population of innovation-driven economies.

The availability of a sample at an international level with this degree of representativeness allows us to analyze the influence that the economic, social, and institutional environment exerts on the behavior of social entrepreneurs and the differences that exist between them and commercial entrepreneurs.

3.2. Estimation Strategy

The empirical analysis is carried out by applying multinomial logistic regressions, where the dependent variable describes a response in the form of a set of possible events (to be a commercial entrepreneur, to be a social entrepreneur or not to be an entrepreneur). The functional form of this model is as follows:

$$\mathrm{Pr}\left(\mathrm{y}=\mathrm{j}/\mathrm{x}\right)=\frac{ \exp \left({\mathrm{x}}^{\prime }\mathrm{Bj}\right)}{{\sum }_{\mathrm{j}=0}^2\exp ({\mathrm{x}}^{\prime }\mathrm{Bj})}$$

where

yj—Dependent variable that can take the value 0, 1, or 2 in the case of “non-entrepreneurial population”, “commercial entrepreneurs”, or “social entrepreneurs”, respectively.

x—Vector of independent variables.

Bj—Coefficients for the independent variables.

The Bj parameters enable evaluating the influence of each independent variable on the probability that these three described situations occur (non-entrepreneur vs. commercial entrepreneur vs. social entrepreneur). However, in order to facilitate the interpretation of the results, the relative risk ratio (RRR) is used, which is a measure of association between the dependent variable (Y) and the independent variable (X) that shows the frequency of the analyzed event (Y = 1 or Y = 2) with respect to the reference event (Y = 0), considering these explanatory variables. The use of the relative risk ratio (RRR), in addition to facilitating interpretation, enables comparing the magnitude of the relationships.

Once the model has been defined, the measurement, concept, and source of data from which each of the variables come from are detailed, as shown in

Table 2. Variables.

Variable	Description	Source
Dependent Variable
ENTREPRENEURSHIP	Non-entrepreneurial population (level 0), commercial entrepreneurs (level 1: TEA) or social entrepreneurship (level 2: SEA)	GEM (APS)
Values and Attitudes to Start a Business
KNOWENT	People who know someone related to entrepreneurship	GEM (APS)
OPPORT	People who believe that there is an opportunity to start a business	GEM (APS)
SUSKILL	People who have the appropriate knowledge to start a business	GEM (APS)
FEARFAIL	People who relate failure with an obstacle to start a business	GEM (APS)
Personal Characteristics of the Entrepreneur
GENDER	Gender: female	GEM (APS)
AGE1	Age: less than 34	GEM (APS)
AGE2	Age: between 34 and 54	GEM (APS)
AGE3	Age: more than 54	GEM (APS)
EDUC1	Primary education	GEM (APS)
EDUC2	Secondary education	GEM (APS)
EDUC3	Higher education	GEM (APS)
Entrepreneurial Environment
NES	Expert opinion on the entrepreneurial environment	GEM (NES)
EASYSTART	Procedures, time, and money involved in creating a new business	DB
Financial and Institutional Environment
IDE	Innovation-driven countries	GEM (APS)
CRDT	Ease of obtaining loans thanks to legal protection and degree of credit diffusion	DB
FS	Development of the financial system	GFD

.

The dependent variable (ENTREPRENEURSHIP) can take three possible values in a single multinomial variable (non-entrepreneurial population, commercial entrepreneurs and social entrepreneurs). This information comes from the APS survey of the GEM.

We classify the explanatory variables into internal factors (the entrepreneur) or external factors (the entrepreneurial ecosystem). The internal factors include values, attitudes, and entrepreneurial skills [48,49,50] and the entrepreneur’s personal characteristics [28,30,51,52]. The external factors include the entrepreneurial environment [32,53,54,55] and the financial and institutional environment [21,27,38,40,56].

The values and attitudes to start a business are dummy variables that take the value of 1 if a sample observation satisfies the characteristic and 0 otherwise. The values and attitudes to start a business include four explanatory variables, which are also obtained from the APS. The first one is the Entrepreneurship network (KNOWENT), which considers the individuals who know people involved in entrepreneurial initiatives. The second one is the perception of opportunities (OPPORT), which considers individuals who perceive opportunities to start a business in the next 6 months. The third one is entrepreneurial skills (SUSKILL), which includes individuals who perceive having the knowledge and skills necessary to start a business. Finally, the fear of failure variable (FEARFAIL) includes individuals who consider fear of failure an obstacle to start a business.

The personal characteristics of the entrepreneur are dummy variables, too. In this case, we include three different explanatory variables. Firstly, we consider the gender of the individual (GENDER), which is measured as a dummy variable that takes value 1 in the case of women and zero for men. Secondly, we consider the age, including three levels: less than 34 (AGE1), between 34 and 54 (AGE2), and more than 54 (AGE3). The inclusion of these three levels follows the classification proposed by [49], which distinguishes three different phases with a non-linear behavior (young adulthood, middle adulthood, and late adulthood). To set the cut-off points for each of the groups, the reference levels proposed by [8] have been followed. Each of them is considered a different variable in the model, which is defined under the same designation of the joint variable “Age”. To control the effect of multicollinearity, the last two levels are incorporated into the model (individuals under 34 are the base level). Finally, we include the educational level completed by the individual, which is also grouped into three different levels: primary (EDUC1), secondary (EDUC2), and higher (EDUC3). The last two groups are included in the model, being the base category in this case, those whose educational level is “primary”.

The entrepreneurial ecosystem includes two categories of external factors: the entrepreneurial environment and the financial and institutional environment.

The entrepreneurial environment includes two different variables, which are related to the general conditions that affect the entrepreneur’s activity. The first one is based on the National Expert Survey (NES), and it is the average of the opinion that qualified experts have on various factors that affect the entrepreneurial environment. Specifically, the pillars that support the NES are financing for entrepreneurs, government policies (referring to priority and support, and bureaucracy and taxes), government programs, education and entrepreneurial training (school and post-school stage), the transfer of Research and Development, access to commercial and professional or physical infrastructure, and services, the dynamics and barriers of access to the internal market and, finally, social and cultural norms. The second variable is related to how easy it is to start a new business (EASYSTART), and it considers the procedures necessary for the creation of a new company as well as the time and the cost of starting a new business.

Finally, the financial and institutional environment includes three variables, each of them from a different data source. The first one is the level of development of the country (IDE), which refers to the classification of countries of the World Economic Forum (WEF) and discussed earlier in the composition of the sample. This is a dummy variable that takes the value 1 for the category of “Innovation-driven economies” and zero otherwise. The second one is related to access to credit (CRDT) and uses the Doing Business variable that measures the degree of inclusion in the laws of the characteristics that facilitate loans and the degree of information dissemination related to credits. The last variable considers internal credit to the private sector (FS), and it is a quantitative variable obtained from the Global Financial Development database (GFD) that includes the financing provided to the private sector by financial institutions (calculated as a percentage of Gross Domestic Product).

4. Results

This section presents the results of the empirical analysis carried out. In the first place, we begin by presenting the results of a descriptive analysis, in which we analyze the values of the different variables studied according to the level of development of the countries. Later, we present the results of the multivariate analysis, following the distribution of the variables shown in Table 2.

4.1. Descriptive Analysis

The descriptive analysis allows for an initial approximation and serves as a basis for the subsequent multivariate analysis. For this,

Table 3. Descriptive analysis.

Variable	Non-Innovation Driven			Innovation-Driven			Z	Prob > |z|
	Observations	Mean	Standard Deviation	Observations	Mean	Standard Deviation
TEA	78,961	0.176	0.381	59,210	0.074	0.262	55.191	0.000
SEA	78,961	0.081	0.273	59,210	0.054	0.227	19.386	0.000
KNOWENT	78,961	0.447	0.497	59,210	0.327	0.469	44.797	0.000
OPPORT	78,961	0.450	0.498	59,210	0.347	0.476	38.608	0.000
SUSKILL	78,961	0.565	0.496	59,210	0.440	0.496	45.848	0.000
FEARFAIL	78,961	0.367	0.482	59,210	0.427	0.495	−22.750	0.000
GENDER	78,961	0.502	0.500	59,210	0.492	0.500	3.797	0.000
AGE2	78,961	0.394	0.489	59,210	0.444	0.497	−18.648	0.000
AGE3	78,961	0.160	0.367	59,210	0.238	0.426	−36.142	0.000
EDUC2	78,961	0.630	0.483	59,210	0.619	0.486	4.160	0.000
EDUC3	78,961	0.197	0.398	59,210	0.249	0.433	−23.338	0.000
NES	35	2.631	0.269	21	2.901	0.322	−3.292	0.001
EASYSTART	35	0.314	0.471	21	0.952	0.218	−4.609	0.000
CRDT	35	57.286	17.122	21	58.810	18.159	−0.331	0.740
FS	35	60.579	38.797	21	105.235	39.123	−3.867	0.000

Source: empirical analyses by the authors.

shows the mean and standard deviation of the variables analyzed, differentiating the innovation-driven economies from the non-innovation economies (bases of factor or efficiency), as well as a test of difference between means to check if the situation in each type of country has significant differences.

The results of the descriptive analysis show as [46] that practically all the variables have statistically significant differences depending on the level of development of the country. Regarding the type of entrepreneurship, both the average of the commercial entrepreneur (TEA) and that of the social entrepreneur (SEA) are significantly higher in non-innovation driven countries. These differences are linked to those that appear in the values and attitudes to start a business [13], which include the entrepreneurial network (KNOWENT), the perception of opportunities (OPPORT), having skills (SUSKILL), and fear of failure (FEARFAIL), since in all of them, the effect is also more important in non-innovation driven countries (greater value in the first three and lower in fear of failure, which has a negative effect and therefore requires an inverse interpretation).

Secondly, the entrepreneur’s personal characteristics, which include gender (GENDER) [22,23], age (AGE) [24,25,26,27,28], and education (EDUC) [30,31,32], also show significant differences between the two categories of countries. In innovation-driven countries, the proportion of women is slightly lower, as well as an older population with a higher level of education.

Thirdly, the entrepreneurial environment [53,54,55], which is formed by the opinion of qualified experts (NES) and the facilities to start a business (EASYSTART), also shows significant differences, since the average of both variables is higher in innovation-driven countries.

Finally, in the financial and institutional environment variables [42], only the development of the financial system (FS) has significant differences, showing a higher average in innovation-driven countries.

4.2. Multivariate Analysis

The multivariate analysis is carried out based on a general model (model 1), which includes the entire sample of countries and later differentiates the situation in non-innovation driven countries (model 1.1) and in innovation-driven countries (model 1.2).

The results (

Table 4. Multivariate analysis results.

Model (1)						Model 1.1 (Non-Innovation Driven Countries)					Model 1.2 (Innovation-Driven Countries)
	Commercial	Social	Dif.	Z	Sig.	Commercial	Social	Dif.	Z	Sig.	Commercial	Social	Dif.	Z	Sig.
	(Y = 1)	(Y = 2)				(Y = 1)	(Y = 2)				(Y = 1)	(Y = 2)
KNOWENT	2.129 ***	1.898 ***	−10.85%	−4.08 ***		1.962 ***	1.898 ***	−3.30%	−0.99		2.618 ***	2.054 ***	−21.52%	−4.70 ***
OPPORT	1.689 ***	1.737 ***	2.84%	1.01		1.612 ***	1.617 ***	0.29%	0.09		1.837 ***	1.695 ***	−7.74%	−1.57
SUSKILL	4.471 ***	2.038 ***	−54.41%	−23.62 ***		3.995 ***	1.976 ***	−50.55%	−17.5 ***		5.893 ***	2.141 ***	−63.67%	−16.49 ***
FEARFAIL	0.717 ***	0.810 ***	12.88%	4.23 ***		0.774 ***	0.845 ***	9.25%	2.58 **		0.600 ***	0.796 ***	32.73%	5.27 ***
GENDER	0.897 ***	0.889 ***	−0.91%	−0.35		0.925 ***	0.847 ***	−8.52%	−2.81 ***		0.803 ***	0.975	21.41%	3.87 ***
AGE
Under 34	Base					Base
Between 34 & 54	0.950 ***	1.043 *	9.81%	3.26 ***		0.926 ***	1.059 *	14.45%	3.97 ***		1.017	1.051	3.43%	0.61
Over 54	0.525 ***	1.031	96.48%	16.22 ***		0.544 ***	1.031	89.57%	12.53 ***		0.478 ***	0.987	106.3%	9.76 ***
EDUCACIÓN
Primary	Base					Base
Secondary	0.996	0.993	−0.34%	−0.09		0.937 **	0.891 ***	−4.88%	−1.12		1.301 ***	2.076 ***	59.57%	3.88 ***
Higher	1.137 ***	1.784 ***	56.88%	10.20 ***		0.948	1.296 ***	36.63%	6.12 ***		1.771 ***	4.289 ***	142.2%	7.20 ***
NES	1.266 ***	1.180 ***	−6.75%	−1.37		1.177 ***	0.640 ***	−45.65%	−9.31 ***		0.956	2.280 ***	138.6%	8.85 ***
EASYSTART	0.987	1.083 ***	9.75%	2.66 ***		0.943 **	1.126 ***	19.41%	5.08 ***		1.268 ***	1.669 ***	31.65%	4.78 ***
IDE	0.560 ***	0.801 ***	42.91%	9.00 ***
CRDT	1.030	0.580 ***	−43.69%	−13.11 ***		0.993	0.496 ***	−50.04%	−12.9 ***		1.034	0.714 ***	−30.95%	−4.42 ***
FS	0.816 ***	0.749 ***	−8.30%	−3.51 ***		0.832 ***	0.828 ***	−0.42%	−0.16		0.869 **	0.653 ***	−24.86%	−3.87 ***
CONSTANT	0.046 ***	0.621 **				0.076 ***	4.597 ***				0.020 ***	0.021 ***
Observations	138,171					78,961					59,210
Pseudo R2	0.1205					0.0936					0.1446
Mean VIF	1.40					1.25					1.37

Level of significance: *** 0.01; ** 0.05; * 0.1. KNOWENT: entrepreneurial network; OPPORT: opportunity to start a business; SUSKILL: skills and knowledge to start a business; FEARFAIL: fear of failure; GENDER: male or female; AGE: age intervals; EDUCATION: education level; NES: entrepreneurial environment; EASYSTART: barriers to start a business; IDE: country category (innovation-driven); CRDT: degree to which the law protects the rights of lenders and facilitates financing; FS: development of the financial system; Dif.: percentage difference over commercial entrepreneurs; Z: linear restrictions test to compare the coefficients of commercial and social entrepreneurs.

) show the relative risk ratio and the level of significance for each independent variable and type of entrepreneurship (commercial and social). Additionally, the difference between the ratios (in relation to the commercial entrepreneur) and the z-value, and the level of significance of the differences between the two types of entrepreneurship are included. In addition, the number of observations, the pseudo R2, and the mean of the variance inflation factor (VIF) are detailed in the lower part of the table.

To control that the results are not affected by multicollinearity problems, the variance inflation factor (VIF) of the models has been calculated. A serious multicollinearity problem is considered if the VIF of one variable is greater than 10 or if the average of all the VIFs is considerably greater than 1 [57]. In this case, the VIF of the joint model is considerably below 10 in all variables (the maximum value is 2.07) and the average VIF is 1.40, so it can be affirmed that there are no multicollinearity problems. The same situation is observed considering the country categories, where the VIF is less than 10 (the maximum values being 1.89 and 2.42) and the average has values of 1.25 and 1.37, respectively in each model.

The results in Table 4 allow us to analyze how the explanatory variables affect each type of entrepreneurship with respect to the common base category “No entrepreneur” (significance and value of the RRR) and, in a complementary way, to compare the statistically significant differences between the two types of entrepreneurship (commercial and social). The differentiated analysis according to the level of development of the countries provides additional evidence to [46], enabling us to study to what extent the type of country conditions the results obtained in the general model.

4.2.1. Values and Attitudes to Start a Business

Regarding the first group of factors [48,49,50], which are the values and attitudes to start a business, all the variables are statistically significant (model 1). In the same way as [13], all of them affect both types of entrepreneurship in the same way, that is, the entrepreneurial network, the detection of opportunities, and having skills positively influence both commercial and social entrepreneurs (RRR greater than 1), while the fear of failure has a negative influence on entrepreneurship (RRR smaller than 1). Therefore, comparing the two types of entrepreneurship with each other, the differences do not lie in the sign of the relationship but in the magnitude. In all cases, the effect is greater for commercial entrepreneurs, except for the perception of opportunities that, as reflected in the level of significance of the Z-statistic, does not show a significant difference between both forms of entrepreneurship. Thus, individuals who have met other entrepreneurs (KNOWENT) are more than twice as likely to be commercial entrepreneurs (RRR = 2.129), being this difference 10.85% lower in the case of social entrepreneurship (RRR = 1.898). On the other hand, individuals who believe they have the necessary knowledge and skills to start a business (SUSKILL) have a probability of more than four times to be commercial entrepreneurs (RRR = 4.471), while for social entrepreneurs, this effect is reduced to less than half (RRR = 2.038). Finally, fear of failure (FEARFAIL) does not affect social entrepreneurs as strongly (RRR = 0.810), as in the case of commercial ones (RRR = 0.717), although in this case, the difference is reduced to magnitudes more similar to the case of the entrepreneurial network (12.88%).

On the other hand, the analysis by country category shows similar results to those previously mentioned in the general model [46], especially for the category of innovation-driven countries, in which the main results coincide in all the variables. It should be noted that the difference between commercial and social entrepreneurs shown in the general analysis is larger in this category of innovation-driven countries, since the entrepreneurial network (−21.52% versus −10.85% of the general analysis), possession of skills (−63.67% versus −54.41%), and fear of failure (32.73% versus 12.88%) have higher magnitudes in innovation-driven countries than in non-innovation driven countries. It is even observed that in non-innovation driven countries that the entrepreneurial network variable does not show differences between commercial and social entrepreneurs. These results indicate that entrepreneurs in less developed countries perceive a minor difference between commercial and social entrepreneurship, which was probably because in many of these countries, most of the initiatives have a certain social component.

4.2.2. Personal Characteristics of the Entrepreneur

The entrepreneur’s personal characteristics include three factors: gender, age, and education. Regarding gender [22,23], in both types of entrepreneurship, there is a greater presence of the male gender, with no significant differences between commercial and social entrepreneurs. However, there are important differences in the model differentiated by type of country. On the one hand, in the category of non-innovation driven countries, the negative influence of women for both types of entrepreneurship remains the same. However, in this case, there are differences (RRR lower in social entrepreneurship than in commercial entrepreneurship, with a difference of −8.52%), being the negative effect for women greater in social entrepreneurs. On the other hand, the category of innovation-driven countries also indicates that when comparing commercial entrepreneurs with social entrepreneurs, there are differences compared to the general analysis. However, in this case, the negative influence of women is fulfilled only in commercial entrepreneurs, but in social entrepreneurs, it is not significant and, therefore, the significant difference between the two is 21.41%. These results show us how in more developed countries, where women have more possibilities to start a business, their greater concern for social issues makes the difference with men disappear.

On the other hand, age shows as [26,27,28] that commercial entrepreneurship predominates in young people, since it has a significantly negative relationship after the age of 34 (0.950), but it is especially relevant after the age of 54 (0.525). However, this effect is much less relevant in social entrepreneurs, since only a small difference is observed for people over 34 (1.043), which disappears once they reach 54. Similarly, differentiation by country gives rise to certain discrepancies with respect to the general model, but these differences are only in the category of innovation-driven countries. In these countries, only individuals aged over 54 have a negative relationship with respect to commercial entrepreneurship, so the negative effect of the previous interval (34–54) is no longer observed. As for social entrepreneurship, no significant relationship is observed in any age range, so the positive relationship between individuals 34–54 years and social entrepreneurship is no longer significant.

Finally, education as [30,31,32,33] shows a positive relationship for both types of entrepreneurship only in the case of higher education. Therefore, the propensity of the individual to engage in commercial or social entrepreneurship increases with a higher education, although this influence is significantly greater for the social entrepreneur, with a difference of 57%. In the same way as with the previous variables, there are certain differences if the general model is differentiated by country category. On the one hand, the category of non-innovation driven countries shows that for commercial entrepreneurs, only secondary studies have a negative relationship. In the case of the social entrepreneur, an even greater negative effect of secondary education is observed, but for this type of entrepreneurship, higher studies continue to exert a positive influence. In comparison with the general analysis, both types of entrepreneurship differ in the influence of secondary studies (in the joint model, it was not significant) and in the non-significance of higher studies, in the case of commercial entrepreneurs only. On the other hand, the category of innovation-driven countries has a positive relationship for secondary and higher education in both types of entrepreneurship. In addition, the difference between commercial and social entrepreneurs is significant, since this positive effect is greater for social entrepreneurs. Note that thanks to the differentiated model, it is observed that the greatest positive effect of higher studies on the social entrepreneur is mostly justified by innovation-driven countries, where the RRR is approximately three times higher than the RRR of the commercial entrepreneur.

4.2.3. Entrepreneurial Environment

The third group, the entrepreneurial environment [53,54,55], includes two variables: the opinion of qualified experts on this environment (NES) and the facilities to start a business.

Regarding the NES, in the general model, this variable positively influences entrepreneurship (commercial and social) without significant differences between them. However, in the model differentiated by the level of development of the countries, the results vary considerably. On the one hand, in the category of non-innovation driven countries, there is a negative influence of the NES on social entrepreneurship. In addition, this makes a significant difference of −45.65% appear between the commercial entrepreneur (positive relationship) and the social entrepreneur (negative relationship), which in the general analysis was not observed. In this way, better conditions of the entrepreneurial environment in these countries makes entrepreneurs more oriented to business activities, reducing social activity. On the other hand, the category of innovation-driven countries also shows differences in the influence of the NES on entrepreneurship compared to the general analysis (positive for both) and the previous category (negative for the social one). In this case, for the commercial entrepreneur, the relationship with the NES is not significant, while for the social entrepreneur, it is positive. Therefore, a significant difference between the two of 138.6% can be observed, which stands out considerably over the negative difference observed in the other category. In this case, there is a significant positive influence of the environment on social entrepreneurship compared to commercial entrepreneurship, which may be explained by the very nature of the countries in this category. As we saw in the descriptive analysis, the value that environmental conditions take is greater in innovation-driven countries (see Table 3). Under these conditions, the marginal effect on the commercial entrepreneur could be less strong, since improvements are more difficult to achieve when all the countries have a higher level. However, since the development of social entrepreneurship has been more recent and has had more obstacles for its development, improvements in the environment conditions have a much more relevant effect on its evolution.

Regarding the facilities to start a business, in the general model, they have a positive influence for social entrepreneurs and, on the contrary, it is not a statistically significant variable for commercial entrepreneurs (hence, there is a significant difference of 9.75%). In the case of the differentiated model, it is observed that for non-innovation driven countries, facilities to start a business positively influence social entrepreneurship, as the general analysis showed, but instead, it is not significant for commercial entrepreneurship: there is a small negative influence. Therefore, the differences between both types of entrepreneurship remain significant, being in non-innovation driven countries of 19.41%, more than double than in the general model. In this case, it should be borne in mind that the value is almost one-third of that in innovation-driven countries (see Table 3), with a very high standard deviation, which means that this small difference observed must be analyzed with caution. However, this variable has a positive influence for social and commercial entrepreneurs in the case of innovation-driven countries. Although the influence is positive for both forms of entrepreneurship, the difference remains significant, since the effect of the variable is greater on social entrepreneurship, reaching a 31.65% difference.

A greater effect on social entrepreneurship is observed in all cases, which, as we have already seen, starts from a less advantageous situation than commercial entrepreneurship, which makes the marginal effect of improvements in the environment affect social entrepreneurship more significantly.

4.2.4. Financial and Institutional Environment

Finally, when analyzing the fourth group of variables that shows the financial and institutional environment, we observe unlike [42] that the variables analyzed that have some influence affect entrepreneurship negatively, both commercial and social.

On the one hand, as seen in model 1, both types of entrepreneurship have a greater presence in non-innovation driven countries (the RRR is lower in both cases). However, the effect is higher for commercial entrepreneurship, with a difference of 42.91%. That is, in the case of social entrepreneurship, the differences are reduced, so it has a relatively greater presence in developed countries.

The administrative facilities for obtaining credit affect the probability of being a social entrepreneur negatively, showing almost half the probability of starting a business (0.580). This variable is not significant in the case of commercial entrepreneurs, and therefore, there are significant differences between the two, which represent −43.69% for commercial entrepreneurs. In the model differentiated by countries, this pattern is maintained, although the difference is reduced in the case of innovation-driven countries (being −30.95% compared to −50.04% of non-innovation driven countries). These results show that this type of facility makes credit transfer mostly to commercial activities, whether they are entrepreneurial or not, but not reaching social activities in the same way.

Finally, the development of the financial system, measured as the ratio of private credit proposed by [58], has a negative effect on both types of entrepreneurship (commercial or social), but this effect is greater for social (0.749) than for commercial entrepreneurs (0.816) and, therefore, the difference between them is significant and represents −8.30%. In the country-differentiated model, some differences can be observed. In the case of non-innovation driven countries, although the influence remains negative, there is no difference between both types of entrepreneurs. Therefore, the significance of this difference in the general model is given by the significant difference of innovation-driven countries, since the difference in this case between commercial and social entrepreneurs is 24.86%. Again, we see how a higher credit granted by the more traditional means, bank credit, makes the funds mostly be allocated to non-entrepreneurial business activities, being also particularly damaged social activities, which are the least able to take advantage of that higher level of financing by financial institutions.

The results of the financial and institutional environment are relevant because they show that some of the facilities included do not seem to be reaching entrepreneurs, and especially social entrepreneurs. In this way, improvements in credit facilities, both from an administrative point of view and from the volume of available credit, seem to be absorbed by the consolidated business sector. This means that in relative terms, entrepreneurs are negatively affected compared to traditional companies, especially social entrepreneurs. The main implication of these results is that the policies of financial support for entrepreneurship must consider this differential effect if they really want to be effective and end up facilitating the financing of entrepreneurs.

5. Conclusions

Social entrepreneurship emerges as an alternative to face the social and environmental problems currently present in our society. Although the social and solidarity objective of this type of entrepreneurship makes a clear difference regarding the economic objective of the commercial entrepreneur, there are other internal factors, such as the values and personal characteristics of the entrepreneur, and external factors, such as the entrepreneurial, financial, and institutional environment, which condition the behavior of the social entrepreneur. However, the effect of these determining factors may vary depending on the level of development of the country, which means that an analysis that does not consider this differential effect may have important bias in its results.

The present study analyzes the determinants of entrepreneurial motivation considering the different influences of the ecosystem under different levels of development of the country. The study is carried out through two different analyses: the first one shows the effect of the determinants on the commercial and social entrepreneur and the second one reflects the moderating effect of the level of development of the country, differentiating the first analysis in non-innovation driven and innovation-driven economies.

The entrepreneur’s values and attitudes do not show relevant differences between commercial and social entrepreneurs, since in both cases, they are affected in the same way. This causes the differences found to be relative to the magnitude of the influence of each factor but not to different effects. The entrepreneur’s behavior does reflect some differences in personal characteristics (gender, age, and education). The general analysis shows as [22] how the male gender predominates in commercial and social entrepreneurship, but it is observed that this variable is no longer significant for social entrepreneurs in innovation-driven countries, where the incorporation of women into the entrepreneurial phenomenon seems to be encountering fewer obstacles. The general analysis also indicates as [24,25] that commercial entrepreneurs are mostly young and social entrepreneurs between 34 and 54 years old. However, the age of the social entrepreneur is no longer significant again in innovation-driven countries. Finally, there are also differences in the educational levels between both types of entrepreneurs and depending on the level of development of the countries, but in general, there is a greater propensity toward social entrepreneurship in individuals with higher educational levels as evidence found at [30,31,32,33].

Additionally, the results show, as in the research of [41], the effect that the ecosystem (the entrepreneurial, financial, and institutional environment) has on the social entrepreneur’s motivation and differentiate it from the commercial one.

On one hand, we observe the influence of the ecosystem from the point of view of the entrepreneurial environment [34,35,36,37], where we can find the most relevant differences of the study. Observing the results of the general analysis, a better entrepreneurial environment has a positive influence on the commercial and social entrepreneur, with no significant differences between them. However, this effect on the social entrepreneur is negative in non-innovation driven countries and positive in innovation-driven countries, so the influence of the countries’ environment modifies the hidden effect of the joint model. Additionally, the facilities to start a business [29] seem to benefit the social entrepreneur both in less developed and developed countries, since the effect on the commercial entrepreneur in the first category is negative and, although it is positive in the second category, the influence is lower compared to the social entrepreneur.

On the other hand, we analyze the second dimension of the ecosystem, the financial and institutional environment. In this case, a better financial and institutional environment does not seem to be an advantage for the social entrepreneur, since the formal facilities for obtaining credit and the development of the financial system [42] affect this type of entrepreneurship negatively. The effect of this last variable is greater for the social entrepreneur, but when analyzed in less developed countries, the difference between social and commercial entrepreneurs ceases to be significant. These global improvements seem to be more easily transferred to the “traditional” company than to entrepreneurs, although we see that the degree to which this occurs is determined by the development of the country and the type of entrepreneurship.

Therefore, when the development of countries is considered, practically all the variables described by [46] are affected. On the one hand, there are relationships and differences that were not significant, such as the entrepreneurial network, gender, or age, where the results of the joint model are disguising a differential influence related to the development of the country. On the other hand, the magnitude of the relationships in some variables changes, such as in the skills to start a business, the fear of failure, or the variables of the entrepreneurial and financial environment. In this case, the variables related to obtaining credit in innovation-driven countries and the development of the financial system in non-innovation driven countries decrease its negative effect on the social entrepreneur. Finally, even the sign of some relationships changes, as is the case of education or the variables of the entrepreneurial environment. In this case, the influence of the NES stands out, which in non-innovation driven countries negatively influences social entrepreneurs, while it exerts a very significant positive influence in the innovation-driven countries.

The results show the importance of the ecosystem in the analysis of the motivation of social entrepreneurs, which is a fundamental aspect when establishing and evaluating support policies and promotion of this type of activities. In addition, the level of development of countries as it was evidenced by [56] is also essential when analyzing the relevance of the entrepreneurial ecosystem. This result is especially relevant when evaluating the effectiveness of support policies for social entrepreneurial activity, since its influence is strongly determined by the type of country in which they are applied.

Finally, it is important to highlight some limitations of our study. First, we must consider the lack of unanimity regarding the concept of Social Entrepreneurship, so the perception in each country about this type of entrepreneurship can vary. On the other hand, the GEM database is based on surveys carried out on individuals, which incorporates a subjective component to the research. As future lines of research, according to the results of the study, it is necessary to deepen the environmental factors, since they sometimes provide counterintuitive results. This makes it necessary to deepen the measurement of the factors of the entrepreneurial environment with greater precision, as well as to analyze other factors of the financial and institutional environment, such as alternative financing sources or those more specific for entrepreneurial activity.