The Determinants of Success in Entrepreneurship: A Study in the Urban Area of Ecuador

Abstract

This research work aims to identify the main factors that determine the success of the enterprises established in the urban area of Riobamba (Ecuador). The research is descriptive and correlational. The field study includes a survey applied to 57 companies. For the analysis, the econometric model of binary logistic regression is used, taking nine explanatory variables. The main results show that the main determinants of success in ventures are represented as the most significant variable human capital explained by experience, academic education, and age; the greater the human capital, the greater the possibility of achieving success in ventures. Financial capital is another important variable that significantly determines entrepreneurial activity. Finally, social capital has a direct relationship with the success of ventures.

1. Introduction

At present the labor field is exposed to various constant changes, so adaptability of the work to the needs of the market must be carried out immediately, since companies require the worker to perform adequately in assigned functions, this being one of the reasons why individuals seek new employability alternatives that allow them to achieve a degree of personal self-esteem, social well-being and adequate economy. One of the initiatives to achieve the aforementioned is entrepreneurship, which allows personal, professional and business development [1,2].

The factors that allow success in entrepreneurship are diverse, and include motivation, risk taking, political and economic stability [3,4,5], but the main ones are: (1) human capital explained by age, education, and experience; (2) financial capital obtained through the contributions of partners, loans received by families, and financial experience; (3) finally, social capital explained by family entrepreneurs and contacts with business networks, which are factors that help to show whether the venture has achieved success [3,6,7,8]. That is why, in recent years, governments have recognized the importance of promoting the generation of entrepreneurships, because they contribute to economic growth and also contribute to reducing the unemployment rate [2,6,9,10].

In the last five years, several Latin American countries, such as Peru, Chile, Brazil, Argentina, Uruguay, Colombia, and Mexico, have shown a positive increase in the generation of entrepreneurs, since they have a larger young population willing to start a business, and age is a determining factor for its success [11]. Younger startups have a high degree of ambition, energy, and are more creative, which allows them to overcome their lack of credibility, experience, and enables them to attract funding [12,13,14,15]. However, others indicate that entrepreneurs with the longest experience in the market are generally more reserved and conservative when starting a business [16]. Business success in the region can be explained by several determining factors such as a high level of university education, since knowledge can help overcome different financial difficulties, and in turn entrepreneurs have new business opportunities [5,7,10,14].

Educational institutions such as universities are essential to promoting entrepreneurship as an innovative component [17,18]. Another determining factor is experience, which refers to the skills that the entrepreneur acquires in labor, commercial, and financial fields [19,20]. Experience is a determinant of entrepreneurship that influences the time used in the process of creating a new venture. Cooperation networks are another determining factor of success, since they allow business opportunities, such as accessing resources, financing, and receiving support or professional advice [7,10,21].

Studies carried out in Ecuador indicate that determining factors of success in undertakings that reflect a positive impact include age, which ranges between 17 and 71 years, with 41 years as average. Where women represent 52% and men 48%, another factor that determines entrepreneurship in Ecuador would be the education level. It is also observed that 70.5% of entrepreneurs have experience when starting their business, so this factor would also be considered as one of the determinants of entrepreneurship [22]. However, the literature refers to financing as an important factor in the start-up and permanence of an enterprise in the market [23]. Finally, cooperation networks play an important role in determining the undertakings [24,25].

In the case of Riobamba, there are few studies developed in the entrepreneurship field and, specifically, on the factors that determine the success of this phenomenon. Some studies have established that there are an average of 700 companies [26], and the enterprises registered in the municipality of Riobamba in the last two years reach 57 units [27], of which it is unknown how many units have closed their operations and how many enterprises are in constant growth. The interest of this research is the approach of those business units that remain in the market and identifying the factors that positively influence them to achieve success in the venture and, thus, knowing the determinants in these business units. The study of the enterprises that remain in the market are of great interest. For this reason, the objective of the research is to identify the main determinants of the success of urban enterprises, specifically those established in the municipality of Riobamba, Ecuador. The research is structured in the following sections: in the first part the introduction is presented, and then the studies related to the subject of study are presented, together with the materials and methods, followed by the results, discussion, and conclusions of the investigation.

2. Related Studies

Several points of view have been generated about what the important factors are considered as determinants of success in entrepreneurship. Therefore, there are various theoretical and empirical studies that have caused great expectations on the subject to be investigated, since in recent years entrepreneurship has been considered a key factor to promote economic growth and development in a country [2,8,20,25].

Therefore, there has been extensive research on the subject of entrepreneurship in various disciplines of economics [28,29,30,31], psychology [4,16,32], and history [33,34,35], with economic theory being the one that stands out in the present work. Business behavior can be very complex to understand, because there are several factors that must be taken into account in order to achieve success [8]. The research carried out also indicates that financing and employment status are the highly relevant determinants of success in an enterprise [36,37].

These results are consistent with the Lussier model which allows a better understanding of why some companies are successful and others are not. They identify three variables to measure success, which are (1) the business is suffering continuous losses for more than three years; (2) the business is suffering liquidity problems; (3) the company declares bankruptcy or liquidity. In addition, fifteen variables have been previously identified as factors influential in the success and failure of small businesses, which are: capital, record keeping and financial control, industry experience, management experience, planning, professional advisors, education, staffing, product and service timing, economic timing, years, partners, relatives, minorities, and marketing, concluding that factors such as education, business experience, good business plan and finances have a positive and significant impact on the success of ventures [38].

Similarly, other authors point out that human capital, explained by education, experience, and social capital, explained by family support, socialization with third parties and financial capital, are variables that influence the success of the enterprise [7,23,39]. The determinants of success may vary from country to country, due to economic conditions, market trends, culture, and demographics [4,10,13,15]. The education, financial capital, and experience of an entrepreneur have great importance in the success of an enterprise, while creative self-efficacy and networking are factors that help business success [39,40].

In an empirical study applied to 150 Ecuadorian entrepreneurs, the success factors and the characteristics that differentiate entrepreneurs were analyzed. To determine this research they use a logistic regression model, which refers to a systematic approach focused on the analysis of the entrepreneurial process, concluding that the profile of the entrepreneur is male, with a high academic level, work experience related to different entrepreneurial activities, and resources obtained through their networks of contacts. In addition, the creation of the enterprises had the objective of achieving personal fulfillment, and, in turn, contributing to society [22]. In contrast, the research that was carried out in Ecuador in 2015 indicated that the factors that help ventures to be successful in business management are financial support, capacity to undertake government programs, government policies, education, market opening, and social and cultural norms [41]. Similarly, a study in the city of Cuenca, using a survey of 200 companies, aimed to analyze the positive and negative factors that promote entrepreneurship. The results showed setting goals, persistence, confidence, motivation, education, and experience as positive factors, and access to financing, market conditions, and the legal environment as negative factors [42].

The gender, level of education, socioeconomic level, and entrepreneurial capacity all influence the propensity to undertake a business venture. With respect to success in entrepreneurship, the variables of gender and level of education revealed great statistical significance, since women with a high level of education will have a greater probability of achieving success in their entrepreneurship [24,43]. Similarly, to achieve success in an enterprise, the factors that intervene are many, but those with great impact are age, human capital, education, and the motivation of the entrepreneur [44].

There are limitations or gaps in the approach to the construct of the determinants of entrepreneurship in urban and rural contexts; the latter is even more limited, and in the context of Ecuador these are extremely limited, which constitutes a weakness and at the same time an opportunity to generate projects in this area. Consequently, the present investigation presents a hypothesis that intends to be contrasted: "Human, financial and social capital are the determinants of the success of the enterprise in the urban area of Riobamba."

3. Materials and Methods

The research assumes a qualitative approach with some statistics. It has a descriptive and correlational character; descriptive because it describes the phenomenon of study in the current state of the ventures, and correlational, because it tries to determine the degree of relationship or association that exists between the success of the ventures and its determinants (human, financial, and social capital). The sample comprised 57 companies, being the total registered in the municipality of Riobamba, on which a survey was carried out. The data collection instrument was a questionnaire designed with dichotomous, structured questions, Likert and open scales, which were oriented to the study of the variables that determine the enterprises. The analysis period covers 2019–2020, and the software that allowed the statistical and econometric analysis to be carried out was the Statistical Package for the Social Sciences (SPSS), as well as the contrasting of the hypothesis through the application of the logistic regression model binary. It is important to mention that secondary sources, such as documents and reports from institutions linked to entrepreneurial activity, have been used.

To test the hypothesis "Human, financial and social capital are the determinants of success in entrepreneurship in the urban area of Riobamba", an econometric model of binary logistic regression is applied, which consists of investigating whether one or more independent variables explain the dependent variable. This model has been applied in similar investigations, where education, experience in commerce, a good business plan, family entrepreneurs, and finances have a positive and significant impact on the success of the ventures [6,38,45].

Based on research on the determinants of success in entrepreneurship using binary logistic regression models, they are used when it is intended to explain a characteristic or dichotomous event (yes or no, which are coded with values 1 and 0) and with more than an independent variable or covariates. The fundamental objective is to model how the explanatory variables influence the probability of a particular event occurring. For this, the binary logistic regression model has the following elements: a dichotomous dependent variable Yi, which takes the value of (1) when there is success in the venture and takes the value (0) when there is failure in the venture. In addition, there are some independent variables X₁, X₂, …, X_k that help explain the dependent variable. See

Table 1. Independent Variable: Determinants of Success.

Variable	Indicator	Type of Variable	Abbreviation
Human capital	Age	Discrete Quantitative	ED
	Education	Ordinal Qualitative	EDU
	Experience in the sector	Ordinal Qualitative	EXS
	Experience in trade	Ordinal Qualitative	EXC
	Experience as an employee	Ordinal Quantitative	EXE
Financial Capital	Contribution by partners	Binomial Qualitative	CPS
	Loans received by the family	Binomial Qualitative	PRF
Capital Social	Entrepreneurs of the family	Binomial Qualitative	EP
	Contact with network entrepreneurs	Binomial Qualitative	CER

Source: Own elaboration based on Kamitewoko (2013) [6].

.

In general terms, the binary logistic regression model would be:

$$\mathrm{Pr}\left(entrepreneurship success\right)=\mathrm{Pr}\left(Yi=1\right)=\frac{1}{1+e^{-\left({\beta }_0+{\beta }_1{X}_{1i}+\dots +{\beta }_k{X}_{kn}\right)}}$$

(1)

Replacing the values in the previous equation the study model is obtained:

$$\mathrm{Pr}\left(entrepreneurship success\right)=\mathrm{Pr}\left(Yi=1\right)=\frac{1}{1+e^{-\left({\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+{\beta }_3{X}_3+{\beta }_4{X}_4+{\beta }_5{X}_5+{\beta }_6{X}_6+{\beta }_7{X}_7+{\beta }_8{X}_8+{\beta }_9{X}_9\right)}}$$

(2)

where:

Yi: Success of the ventures, categorized (1) for success and (0) for failure of the ventures.

X₁: Age (ED): The age of the owner of the venture.

X₂: Education (EDU): Refers to the educational level achieved by the owner of the venture.

X₃: Experience in the sector (EXS): Indicates if the owner of the venture has experience working in the same sector in which the venture was started.

X₄: Experience in the trade (EXC): Indicates if the owner of the venture is already familiar with the trade.

X₅: Experience as an employee (EXE): Indicates whether the owner of the venture has had experience as an employee.

X₆: Contribution by partners (CPS): Indicates that the source of financing with which the venture was started was by the contribution of a partner.

X₇: Loans received by the family (PRF): Indicates that the source of financing with which the undertaking started was for the contribution of the family.

X₈: Family Entrepreneurs (EF): The owner of the venture has been influenced by a member of the family who is self-employed.

X₉: Contact with network entrepreneurs (CER): Indicates whether the owner of the venture has contact with other entrepreneurs.

4. Results

Next, the results of the survey applied to the enterprises registered in the Autonomous Government of Riobamba are presented. Descriptive analysis shows that 68.42% of the target population are successful and 31.58% fail. Respondents who reported failure attributed it to unexpected drops in sales, problems with suppliers, excessive loans requested by the entrepreneur from a financial institution, and poor internal organization. The study population comprised 59.65% women and 40.35% men, and the most frequent age range was the 35–44 years interval, which corresponds to 38.60%. With regard to educational level, a significant percentage claimed to have higher education, represented by 47.37%, followed by secondary education, represented by 31.58%. However, those that showed a lower percentage than this corresponded to a postgraduate and a primary education level, with 17.54% and 3.51%, respectively.

At the time of starting the venture, the entrepreneur had a lot of experience in the trade, represented by 45.61%, while 38.60% of the respondents had regular experience in the business sector and 31.58% had experience as an employee. Similarly, 61.40% of the entrepreneurs stated that when they started the venture, they did not have financing from a partner. In contrast, 54.39% of those surveyed indicated that they obtained financial help from family members, and 45.61% stated that they had not received a contribution from a member of their family.

Analysis of the Econometric Model

Binary logistic regression was applied using the Wald method (forward), which is an automatic method that allows the software to fit variables into the model, starting with those variables that have larger, statistically significant regression coefficients. In each step carried out, the coefficients and their level of significance are reevaluated; therefore, those variables that are not statistically significant can be eliminated from the model. The following results were obtained, see

Table 2. Model Summary.

Step	Logarithm of Likelihood 2	Cox and Snell’s R Squared	Nagelkerke’s R Squared
1	64.786 a	0.105	0.147
2	56.623 b	0.224	0.315
3	53.521 b	0.265	0.372

^a. The estimate has ended at iteration number 4 because the parameter estimates have changed by less than 0.001. ^b. The estimate has ended at iteration number 5 because the parameter estimates have changed by less than 0.001.

.

According to the R squared of Cox and Snell, it estimates the proportion of variance of the dependent variable explained by the independent variables; in this case, the model has a value of 0.265, which means that the variation experienced by the dependent variable is explained by 26.5% by the variables that are introduced in the model. Nagelkerke’s R squared is an adjusted version of Cox and Snell’s R squared; in this case it has a value of 0.372. The following is a global fit test of the model known as the Hosmer–Lemeshow test. See

Table 3. Hosmer–Lemeshow test.

Step	Chi Squared	Gl	Sig.
1	4.878	2	0.087
2	11.321	8	0.184
3	6.865	8	0.551

.

The Hosmer–Lemeshow test is a complement to assess the goodness of fit of the logistic model. In this case it has a p = 0.551, which gives sufficient evidence of an adequate fit and a consistent model. Next, the classification table is shown, in which the frequencies in the categories of the dependent variable are considered according to what is observed and predicted in each model, as shown in the following table.

As shown in

Table 4. Classification table.

Observed			Predicted
			Success in Entrepreneurship		Correct Percentage
			Failure	Success
Step 1	Success in entrepreneurship	Failure	7	11	38.9
		Success	7	32	82.1
	Overall percentage				68.4
Step 2	Success in entrepreneurship	Failure	12	6	66.7
		Success	5	34	87.2
	Overall percentage				80.7
Step 3	Success in entrepreneurship	Failure	9	9	50.0
		Success	4	35	89.7
	Overall percentage				77.2

, having a cutoff value of 0.5, there is a 77.2% probability of success in the result of the dependent variable, when we know the reason for success or failure.

In

Table 5. Variables in the equation.

		B	Standard Error	Wald	Gl	Sig.	Exp (B)	95% C.I. for EXP (B)
								Lower	Higher
Step 1 a	Trading experience	0.863	0.359	5.772	1	0.016	2.371	1.172	4.797
	Constant	−2.762	1.473	3.515	1	0.061	0.063
Step 2 b	Sector experience	−0.818	0.325	6.325	1	0.012	0.441	0.233	0.835
	Trading experience	1.165	0.416	7.842	1	0.005	3.206	1.419	7.246
	Constant	−1.519	1.572	0.934	1	0.334	0.219
Step 3 c	Sector experience	−0.915	0.346	7.013	1	0.008	0.400	0.203	0.788
	Trading experience	1.239	0.436	8.080	1	0.004	3.454	1.469	8.118
	Family entrepreneurs	−1.204	0.710	2.877	1	0.090	0.300	0.075	1.206
	Constant	−0.835	1.683	0.246	1	0.620	0.434

^a. Variables specified in Step 1: Trading Experience. ^b. Variables specified in step 2: Experience in the sector. ^c. Variables specified in Step 3: Family Entrepreneurs.

, we observe that the individual variables that are significant to formulate the logistic model are experience in the sector and experience in trade, because the significance is <0.05. The family businessmen variable, with a significance value of less than 10%, is included in the model. Therefore, the variables age, education, experience as an employee, contribution by partners, loans received by the family, and contact with network entrepreneurs, are important to explain the determinants.

The new logistic regression model would be formed as follows:

$$\begin{gathered} \mathrm{Pr}\left(entrepreneurship success\right)=\mathrm{Pr}\left(Yi=1\right)=\frac{1}{1+e^{-\left({\beta }_0+{\beta }_3{X}_3+{\beta }_4{X}_4+{\beta }_8{X}_8\right)}} \\ \mathrm{Pr}\left(entrepreneurship success\right)=\mathrm{Pr}\left(Yi=1\right)=\frac{1}{1+e^{-\left(-0.835-0.915X_3+1.239X_4-1.204X_8\right)}} \\ \mathrm{Pr}\left(entrepreneurship success\right)=\mathrm{Pr}\left(Yi=1\right)=\frac{1}{1+e^{-\left(0.835+0.915X_3-1.239X_4+1.204X_8\right)}} \end{gathered}$$

(3)

This equation allows us to identify what the probability would be that an undertaking will be successful or not. To check the validity of the estimated model, it is assumed that an entrepreneur has had previous experience in the trade at the time of starting their venture.

$$\begin{gathered} \mathrm{Pr}\left(entrepreneurship success\right)=\mathrm{Pr}\left(Yi=1\right)=\frac{1}{1+e^{-\left(0.835+0.915X_3-1.239X_4+1.204X_8\right)}} \\ \mathrm{Pr}\left(entrepreneurship success\right)=\mathrm{Pr}\left(Yi=1\right)=\frac{1}{1+e^{-\left(0.835+0.915\left(0\right)-1.239\left(1\right)+1.204\left(0\right)\right)}} \\ \mathrm{Pr}\left(entrepreneurship success\right)=\frac{1}{1+e^{\left(-0.404\right)}} \\ \mathrm{Pr}\left(entrepreneurship success\right)=\mathrm{Pr}\left(Yi=1\right)=1.667644 \\ \frac{1}{1.667644}=0.5996 \end{gathered}$$

(4)

That is, the 59.96% probability of being successful, or being greater than 0.5, the value of Y would be equal to 1, where 1 is success and 0 is failure.

Regarding the interpretation of the parameters obtained in the binary logistic regression model, it must be borne in mind that the sign of the estimated marginal effects is of great importance because they direct the change that is created by unitary variations in the explanatory variables. That is, the effect on the dependent variable can be known by the sign that the coefficient presents; in this sense the coefficients that show a negative sign express that the variable that accompanies them decreases the probability of achieving success in the undertaking, while those that present positive signs in the coefficients increase the probability of achieving success in entrepreneurship. Therefore, according to the results obtained, the trade experience variable shows a positive sign, which means that the probability of a successful undertaking increases.

Regarding the column Exp (B), it shows the strength of the relationship between the dependent variable and the independent variables; the further away from 1 it is, the stronger the relationship. Values that are less than 1 must be transformed into their inverse and, thus, compare the exponentials of β with each other. To do this, divide 1 by the exponential of β. In this case, it occurs in the variables experience in the sector (1/0.400 = 2.5) and family entrepreneurs (1/0.300 = 3.33).

In this way, the hypothesis formulated in the present investigation is accepted; the variable that determines to a greater extent is human capital, mainly explained by business experience, together with education and age. Likewise, they explain to a lesser extent financial capital and social capital.

5. Discussion

The research identifies the main determinants of the success of the ventures. These are related to human capital, and other investigations agree that human capital is an important element in the configuration of productive units and their structure in the ecosystem of ventures [46,47,48,49], and it allows success or failure to the extent that their capabilities and potential are high or low [50,51]. Additionally, the higher the academic training, the greater the possibility of achieving success in entrepreneurship, because they will have obtained greater knowledge and skills in their academic training. Other investigations have shown similar results [52,53,54] and point out that education is one of the significant determinants for the success or failure of enterprises [55,56].

Likewise, the financial aspect coming from the family involved in the entrepreneurial initiative determines to a large extent the success of the ventures; that is, the entrepreneur’s own resources and their environment. In this regard, research shows that the success and failure based on the financing of ventures with own and family funds are moderately important [57,58,59]. Finally, social capital indicates that success in the venture has a direct relationship with family members and they are part of a network, where the entrepreneur has a greater chance of achieving success. There are studies that pay special attention to social capital as an element that determines the success of ventures [60,61,62,63]; specifically, when an entrepreneur is part of a network, there is a greater probability of success than failure.

6. Conclusions

The research identifies the main determinants of success, and these are represented by human capital, explained by the experience of the entrepreneur, together with higher academic training and age, and experience in the management processes of the family business unit is an important element that determines the success of undertakings. Thus, the greater the experience of the human capital, the greater the possibility of achieving success, and academic training also contributes significantly. In addition, financial capital is distinguished; an investment that comes mainly from the savings of entrepreneurs and the economic support of their families that support these business initiatives. Consequently, the financial capital explained by the investment is constituted as a variable that determines the success of the ventures, although to a lesser extent, the greater the family investment, the greater the probability of success. Finally, social capital is highlighted as a determinant of the success of the ventures. Networks play an important role in the consolidation of strategic alliances that generate economic and social benefits for the productive units, which contribute to the formation of conglomerates and the generation of competitive advantages, albeit to a lesser extent.

An important aspect is that, based on the results obtained in the present investigation based on a sample of 57 enterprises, it would be useful to expand the sample focusing an investigation on several cities or areas of action where there is an important fabric of productive units.

Finally, there may be other variables that explain or determine the success of the ventures. In the survey, it was possible to appreciate that cultural factors may be influencing the success of local ventures, mainly in the gastronomy sector. The context in which the research is carried out can also condition the variables analyzed. It would be convenient to develop a similar study in other areas of Ecuador, for example, the Amazon and the littoral coast.

It is worth mentioning some implications from the results of the research, as they have come from a study carried out in the urban context. This should also be extrapolated to the rural context, with the development of an investigation considering the same variables, which will allow a comparison to be made between the productive units based on the variables that determine success and predict the trends of successful ventures. Likewise, it is important to extend the coverage to a greater number of ventures, as it is possible that there are variations in the factors that determine the success of the ventures.

The research work presents some limitations in relation to the study of trust as an element of the variable social capital, culture or adaptation to technology as determinants of the success of ventures. It is important to develop future research taking into account these elements and others that may be linked to the success of the ventures. These investigations must be focused on the urban and rural context and should take into account a greater number of productive units.