Well-Being and Entrepreneurship Intention: An Empirical Study of New Perspectives

Abstract

The research aims to examine the relationships between the constructs of entrepreneurial intention according to the theory of planned behavior (TPB) model and subjective well-being (SW). The model used considers the works proposed by Liñán and Chen and Oyanedel, Vargas, and Paez, and a questionnaire was applied to 1043 people in an urban population of the three main regions of Chile using multivariate statistical methods for its analysis (structural equation models). The proposed hypotheses are that subjective well-being towards entrepreneurship has a direct and positive effect on entrepreneurial intention (H1), personal attitude towards entrepreneurship has a direct and positive effect on entrepreneurial intention (H2), perceived behavioral control towards entrepreneurship has a direct and positive effect on entrepreneurial intention (H3), and subjective norm towards entrepreneurship has a direct and positive effect on entrepreneurial intention (H4). The results indicate that subjective well-being on entrepreneurial intention shows indirect effects mediated by subjective norm, contributing to the theoretical development concerning well-being incidence on entrepreneurial behavior, providing theoretical elements that can serve as a basis for further strengthening the understanding of the relationships between personal well-being, economic growth, and the harmonious relationship with the environment.

1. Introduction

The objective of this study is to investigate the relationship between the entrepreneurial intention constructs of the theory of planned behavior (TPB) model and subjective well-being, with the aim of finding new ways of understanding the phenomenon of entrepreneurship from the perspective of people’s quality of life, rather than the material benefits it generates, putting in tension the paradigms dominant economic rationality. To explore these relationships, it has been proposed to use multivariate analysis to explore the relationships between the constructs mentioned about entrepreneurial intention (EI), entrepreneurial attitude (PA), subjective norm (SN), perceived behavioral control (PBC), and subjective well-being (SB). A form the theory review, hypotheses have been developed, which were studied by applying a survey to 1043 people, residing in the urban areas of the three biggest regions of Chile (Metropolitan Region, Valparaiso, and Biobio), which seeks to answer the following questions: Is there a relationship between subjective well-being related to entrepreneurial intention? Is there a relationship between entrepreneurial attitude and entrepreneurial intention? Is there a relationship between perceived control and entrepreneurial intention? Is there a relationship between subjective norm and entrepreneurial intention? Is there a relationship between subjective norm and entrepreneurial intention?

In terms of sustainability, the following research relates to the Sustainable Development Goals (SDGs) associated with well-being, which are associated with improved health (SDG 3) and sustainable development through economic growth and decent work (SDG 8) [1].

2. Literature Review

2.1. Entrepreneurship and TPB

Undoubtedly, entrepreneurial behavior is desirable for all countries as it generates greater well-being and quality of life in countries, and its promotion has enabled human development, both globally and locally, allowing for economic development and the development of decent work (SDG 8) [1,2]. All entrepreneurial behavior must be based on the existence of an entrepreneurial intention [3,4,5], defined as the conviction to create a business, as well as the conscious planning for its realization at a future time [6]. In this sense, the mechanisms through which entrepreneurs perceive opportunities and decide to take the entrepreneurial option are intentions that act prior to the start-up of the venture, which makes entrepreneurial intention a predictor variable, in the first link of entrepreneurial behavior [7].

This self-recognition, also known as self-sufficiency, is nothing more than the personal conviction of possessing the capacity to perform actions that allow obtaining the desired results. [8]. This desire to create or not to create a company is then given by factors linked to intentionality, cognitive and attitudinal aspects [9], as well as by the relationship they have with contextual factors such as the value system and social and family environment [10]. According to the theory of planned behavior (TPB), when behavior is rational, the best predictor of entrepreneurial action is intention, a factor that is preceded by three important components: attitude, subjective norms, and perceived control over the behavior [9,11,12,13,14,15,16]. Individuals would develop entrepreneurial behavior by making judgments about their capabilities, predicting probable outcomes, analyzing different situations and actions, evaluating opportunities, and defining environmental constraints. Entrepreneurs possess a way of thinking that emphasizes perceived opportunities over threats, and this process of identifying opportunities is an intentional process [8,17,18,19,20].

Attitudes towards the behavior are individual traits linked to the positive or negative evaluation of the performance of the behavior, in this case, of entrepreneurship [21]. Thus, there would be evaluative judgments made by individuals regarding the realization of entrepreneurial behavior, positive or negative judgments, for or against, etc., regarding the realization of entrepreneurial behavior. Thus, the TPB model is based on the assumption of action as a phenomenon based on individual attitudes; therefore, it is a model fundamentally concerned with attitudes configured by cognitive, affective, and behavioral aspects understood as triggering factors of actions [9,22]. In this sense, the TPB, as well as its precursor, the theory of reasoned action, attitudes determine intention in terms of defining the way the individual feels when carrying out a certain behavior [21]. Attitudes, which can be positive, negative, or neutral with respect to the behavior in question, acquire a certain character depending on the favorable or unfavorable belief with respect to the consequences of the development of a certain behavior [21]. The second component is linked to subjective norms, defined as the series of convictions and intentions stimulated by social pressure from family networks, friends, or the contextual environment in general. Social norms translate into individual perceptions of values, beliefs, and norms of the environment relevant to the individual, causing him or her to seek to comply with them. Different authors have confirmed this assumption, pointing out the importance of different contextual elements in the variables of the models of intentions [23,24].

Indeed, environmental variables have an impact on the attitude of individuals and also play a moderating role in influencing the intention and decision to start a business. These are factors linked to personal motivations, the desire for independence and self-fulfillment, as well as external triggering events such as a layoff or lack of promotion within a company. Another important factor is perceived behavioral control (efficacy), defined as the perceived ability to perform certain behaviors, accounting for available resources and opportunities, according to which individuals develop judgments about the likelihood of achievement or failure of the behavior [9,22]. Self-efficacy then determines what the individual thinks he/she is capable of doing for him/herself, being defined as a belief about a certain behavior, associating the performance of such behavior with certain results [25].

In essence, it is people’s perceptions, channeled through their intentions, that can drive or inhibit the identification of new entrepreneurial opportunities in the early stages of the process of creating a company [26].

2.2. Entrepreneurship and Well-Being

Researchers indicate that entrepreneurship is a social phenomenon in which actors relate objectives, wishes, and expectations to their actions in the world. Entrepreneurship facilitates a person’s psychological satisfaction and affects psychological and subjective well-being (SDG 3) [1,27,28,29,30,31]. Wiklund et al. [32] argue that entrepreneurial well-being is “the experiences of satisfaction, positive affect, infrequent negative affect, and psychological function in relation to the development, start-up, growth, and management of an entrepreneurial venture”. Well-being subjectively and objectively influences people’s life experiences [32,33].

This is possible because there are external and internal factors in the conditions that people can perceive as their well-being. The differences in the perceptions of entrepreneurial well-being will depend on the person’s own evaluation may consider more positivist paradigms oriented towards objective measurement as well as the acceptance of subjectivity in the person [31,34]. More specifically, researchers in psychology define well-being in terms of subjective well-being (SB), as the overall internal state of mental well-being, which includes or excludes the realization of pleasure and the avoidance of pain. Subjective well-being is what some researchers refer to as hedonic or desire-based well-being and can be based on life satisfaction, positive affect, and negative affect [35,36].

The first dimension is based on the individual’s own life in positive terms, the second dimension is based on the individual’s perception of his or her positive moods, and finally, the dimension based on negative effect is linked to the individual’s perception of the harmful moods he or she experiences. This takes into account that individuals have certain innate psychological needs that will be the basis of their self-motivated and integrated personality and also takes into account the influence of the social context where each of these individuals is located; however, if they do not provide development, they will favor the alignment and illness of the subject [35,36,37,38,39]. It is in this sense that the present research seeks to explore empirically the relationship between entrepreneurial intention and people’s self-perception of subjective well-being. It is important to note that sustainable well-being is achieved when improved individual well-being (such as subjective well-being) correlates with improved well-being of other members of society and the natural environment. This holistic definition, which is referred to as “sustainable well-being”, is compatible with both complex systems thinking [40] and positive psychology and environmental sustainability [41].

Thus, according to the considerations set out in Section 2.1, Entrepreneurship and TPB, and Section 2.2, Entrepreneurship and Welfare, and based on the considerations set out above Krueger et al. [8,24,26], Contreras et al. [4,33], Liñán et al. [6,26,42], Wiklund et al. [32], Diener et al. [35], and Ajzen [9,21,25], it is possible for us to formulate the following hypotheses:

Hypothesis 1 (H1).

Subjective well-being (SB) towards entrepreneurship has a direct and positive effect on entrepreneurial intention.

Hypothesis 2 (H2).

Personal attitude (PA) towards entrepreneurship has a direct and positive effect on entrepreneurial intention.

Hypothesis 3 (H3).

Perceived behavioral control (PBC) towards entrepreneurship has a direct and positive effect on entrepreneurial intention.

Hypothesis 4 (H4).

Subjective norm (SN) towards entrepreneurship has a direct and positive effect on entrepreneurial intention.

3. Materials and Methods

The population of this study is composed of adults aged 18 or over, residing in the urban areas of the three biggest regions of Chile (conurbations of great Valparaiso, great Santiago, and great Concepcion, as defined by the Chilean National Institute of Statistics). These three regions account for 60% of the national urban population according to the 2017 census.

The total sample is composed of 1043 respondents. Respondents were selected through a probabilistic and multistage sampling method based on the Chilean National Institute of Statistics’ sampling framework. The sample selection process followed three stages: (1) the first stage involved the selection of housing blocks, which were randomly selected from the sampling framework’s master list; (2) once the housing block was selected, an interviewer visited the housing block and, starting from the northeast corner, selected an initial household based on a Kish table. After this first selection, the interviewers had the instruction to follow with a systematic selection based on the fourth household on the left of the last interviewed house, turning around the corner if necessary. This was done until a maximum of 10 households per block were interviewed; (3) inside each household, a member was selected using a Kish table. The sample has an associated error of ±3.5% for a confidence level of 95%. To correct for design effects, the final sample was weighted using the proportions of the 2017 census for these three areas (last current census).

Interviews were face-to-face directed by a trained interviewer and cards to support answering the different scales. To examine the five domain constructs (entrepreneurial intention, entrepreneurial attitude, subjective norm, perceived behavioral control, and subjective well-being) and the relationships between them, structure equation modeling (SEM), a multivariate analysis technique, was applied as it is an appropriate technique in social science studies [43]. MPlus 8.7 software was used for the analysis.

For the purposes of this study, the five constructs of the entrepreneurial intention model were measured through Likert-type scales (see Appendix A). The methodology presented by Liñán and Chen [42] and translated into Spanish and validated by Laguia et al., [44] and Oyanedel et al. [45], was used, respecting the subjective binomial scales and complementing this analysis with other methods proposed in the literature [46,47].

As proposed by Chandler and Lyon [48], for the purposes of this research, the reliability and validity of the scales will be the most important psychometric properties to be tested. The reliability or internal consistency of a scale will be analyzed through Cronbach’s alpha and the composite reliability index (CR), whose value should be equal to or greater than 0.7 according to [49,50], consider that this property represents the instrument’s capacity to generate the same results each time it is applied to the same person and under the same conditions. When a scale is measured by multiple indicators, a well-known measure, widely used in the social sciences, is Cronbach’s alpha [51]. Although Cronbach’s alpha is relatively simple to calculate, it does not always give an adequate estimate of the scale’s reliability. If the indicators, for example, are non-equivalent or non-parallel measures, which is frequent in applied research, this coefficient could underestimate the reliability of the scale when the error measurements of the corresponding indicators are uncorrelated. Conversely, when measurement errors are correlated, this coefficient may vary the reliability of the scale as a function of the estimated parameters [52]. One way to overcome this weakness of Cronbach’s alpha coefficient as a measure of the reliability of a scale is by calculating a composite reliability index, estimated from the evaluation of the measurement model by means of a confirmatory factor analysis [53,54] based on structural equation modeling (SEM).

To assess the validity of the scales, we proceeded as suggested by Chandler and Lyon [48], and to validate the instrument we performed a confirmatory factor analysis (CFA) [55]. All items were found to be significant and the goodness-of-fit indices met the criteria adequately: the comparative fit index (CFI) gave a value of 0.90 [56,57], the root mean square error of approximation (RMSEA) gave a value of 0.103 [57,58], the Tucker Lewis index (TLI) gave a value of 0.90 [57,59], and the standardized square root residual (SRMR) gave a value of 0.05 [60,61,62,63,64]. The convergent and discriminant validity of the scales was evaluated. As for convergent validity, the factor analysis method was applied as shown in

Table 1. Item–construct correlation matrix.

	Factor
Item	EI	PA	SN	PBC
EI_1	0.931	−0.715	0.541	0.622
EI_2		−0.823	0.600	0.698
EI_3		−0.827	0.609	0.695
EI_4		−0.823	0.609	0.691
EI_5		−0.811	0.609	0.725
EI_6		−0.811	0.611	0.724
EI_7		−0.768	0.579	0.702
PA_1	0.776	−0.919	0.659	0.664
PA_2	0.849		0.727	0.649
PA_3	0.800		0.735	0.627
PA_4	0.803		0.727	0.643
PA_5	0.823		0.686	0.686
PBC_1	0.593	−0.707	0.927	0.543
PBC_2	0.587	−0.716		0.521
PBC_3	0.582	−0.677		0.521
PBC_1	0.665	−0.654	0.555	0.923
PBC_2	0.727	−0.670	0.564
PBC_3	0.706	−0.644	0.548
PBC_4	0.689	−0.593	0.508
PBC _5	0.679	−0.588	0.498
PBC_6	0.726	−0.695	0.582
EI	1.000	−0.847	0.626	0.737
PA	−0.847	1.000	−0.749	−0.666
SN	0.626	−0.749	1.000	0.562
PBC	0.737	−0.666	0.562	1.000

Extraction method: principal axis factorization. Rotation method: oblimin normalization with Kaiser.

, showing that the average of the factor loadings of the items with the factor is greater than the individual loadings of the other items with the factor. The factor analysis method is usually applied in this type of analysis [42]. In the case of our sample, the Kaiser–Meyer–Olkin test was applied to evaluate the sample adequacy for factor analysis. At the same time, Bartlett’s test of sphericity was significant (p < 0.000). According to these results, it can be concluded that the sample data are, in this case, adequate for the application of factor analysis. Next, at this stage of the analysis, and with the purpose of verifying the normality of the distribution of the scale indicators, the Kolmogorov–Smirnov normality test was performed, and no empirical support was found for the null hypothesis of normality. To evaluate the validity of the scales from the examination of correlations, adopting as a criterion that each indicator correlates more strongly with its own construct than with any other, thus allowing respondents to perceive that each indicator belongs to its theoretical construct and not to another [65]. To this end, the indicator–construct correlation was calculated for each factor. After the validity analysis of the scales, an EFA (exploratory factor analysis) was performed to identify the most appropriate indicators for each scale. Given that the normality test of the distribution of the indicators was not supported, in accordance with this result and with what is suggested in the literature, the extraction method selected to perform the exploratory factor analysis was principal axis factorization.

4. Results

To begin with the analysis of results we will start with the Kaiser–Meyer–Olkin (KMO) test, which is a measure of the suitability of the data for factor analysis, measuring the sampling adequacy for each variable in the model and for the whole model. The results show KMO = 0.959. As shown in Table 1, the correlations of each indicator to the other constructs are consistently lower than the mean correlation to its own construct for the factors of the TPB base model.

Table 2. Matrix of rotated factors and reliability of scales.

	Factor
Item	EI	PA	SN	PBC
EI_1	0.707
EI_2	0.923
EI_3	0.939
EI_4	0.983
EI_5	0.923
EI_6	0.985
EI_7	0.844
PA_1		−0.621
PA_2		−0.717
PA_3		−0.935
PA_4		−0.922
PA_5		−0.747
SN_1			0.825
SN_2			1.030
SN_3			0.894
PBC_1				0.776
PBC_2				0.893
PBC _3				0.936
PBC_4				0.946
PBC_5				0.960
PBC_6				0.808
α of Cronbach	0.979	0.966	0.947	0.971

Extraction method: principal axis factorization. Rotation method: oblimin normalization with Kaiser. a: the rotation has converged in 9 iterations.

shows the rotated factor matrix, which shows the factor loadings of each indicator with its expected factor for the TPB base model, consistent with the adopted theoretical basis and empirical results of previous studies.

Then, based on the Cronbach’s alpha coefficient criterion, the reliability of the proposed scales was first tested, considering α = 0.7 as the minimum threshold, a value commonly proposed in the literature for the development of new measurement scales [43]. As a result of this analysis, adequate Cronbach’s alpha values were obtained.

However, the original versions of the scales were modified in order to achieve a maximum α [49], successively eliminating from the scale those indicators that worsened the α.

Table 3. Modified rotated factor matrix and reliability of the scales.

	Factor
Item	E1	PA	SN	PBC
EI_2	0.934
EI_3	0.953
EI_4	0.978
EI_5	0.888
EI_6	0.952
PA_2		−0.691
PA_3		−0.951
PA_4		−0.998
PA_5		−0.739
SN_1			0.827
SN_2			1.029
SN_3			0.894
PBC_2				−0.860
PBC_3				−0.916
PBC_4				−0.985
PBC_5				−0.998
PBC_6				−0.792
α de Cronbach	0.984	0.967	0.947	0.972

Extraction method: Principal axis factorization. Rotation method: Oblimin normalization with Kaiser. The rotation has converged in 8 iterations.

shows the new and final version of the scales after this process.

Finally, for the case of the SB scale, the reduction of dimensions resulted in obtaining a single factor of eight indicators with a Cronbachs alpha α = 0.913.

4.1. Entrepreneurial Intention Measurement Model

At this stage of the analysis, the evaluation of the measurement model of the five scales was performed, based on a confirmatory factor analysis SEM (CFA-SEM), which allowed estimating the composite reliability index of the scales and the goodness-of-fit indices of the measuring model (Figure 1), as proposed by Wang and Wang [47]. As mentioned above, the sample data do not follow a normal distribution, the estimation method applied was the robust maximum likelihood (MLR) method for the non-normality of the variables [46]. The composite reliability indices present adequate values as recommended (ρ between 0.983 and 0.912), above the minimum recommended (ρ > 0.7). The extracted variance index (EVI) also shows satisfactory values (EVI between 0.919 and 0.567), above the minimum suggested (EVI > 0.5), as shown in

Table 4. Matrix of factor loadings and composite reliability indices and variance extracted.

	Factor
Item	EI	PA	SN	PBC	SB
EI_2	0.945
EI_3	0.952
EI_4	0.967
EI_5	0.958
EI_6	0.970
PA_2		0.939
PA_3		0.952
PA_4		0.953
PA_5		0.913
SN_1			0.904
SN_2			0.975
SN_3			0.906
PBC_2				0.953
PBC_3				0.962
PBC_4				0.907
PBC_5				0.901
PBC_6				0.910
SB_1					0.815
SB_2					0.841
SB_3					0.709
SB_4					0.823
SB_5					0.776
SB_6					0.709
SB_7					0.691
SB_8					0.631
IFC	0.983	0.968	0.950	0.968	0.912
IVE	0.919	0.882	0.863	0.859	0.567

Note: CR: composite reliability index; EVI: extracted variance index.

. For their part, the goodness-of-fit indices of the measurement model are above the suggested minimum values (χ2 = 788.420; df = 261; p < 0.0000; RMSEA = 0.044, CI90%: 0.041–0.048; CFI = 0.973; TLI = 0.969; SRMR = 0.036), based on what is presented in the literature [47] for this type of analysis. In accordance with the theoretical development of the scales, and although these psychometric properties should be tested again in other samples, the results presented above suggest that the measurement instruments developed in the framework of this study adequately meet the required reliability and validity properties.

Regarding the measurement model, only the standardized factor loadings that were significant are presented in Figure 1 (only significant coefficients are shown). As expected, the model shows that the covariances between the five constructs are non-zero and significant (p < 0.000), indicating that they are correlated. With respect to the relationship between the constructs and their respective indicators, the fit indices and the respective factor loadings confirm that each indicator saturates to a greater extent with its own construct than with the others, although there are significant correlations between indicators of different constructs and between indicators with other constructs. Both issues are consistent with the results of previous studies.

4.2. Entrepreneurial Intent Structural Model

At this stage of the analysis, the evaluation of the structural model was carried out based on an analysis of covariance structures using SEM structural equation modeling [46]. The purpose of this section is to empirically test the hypotheses proposed in this research. Since, as mentioned above, the sample data do not follow a normal distribution, the estimation method applied was the robust maximum likelihood (MLR) method [46]. The model tested is the one shown in Figure 2 (only significant coefficients are shown). According to the results of the analysis, the goodness-of-fit indices of the structural model are located above the suggested minimum values (χ2 = 668. 791; df = 261; p < 0.0000; RMSEA = 0.039, CI90%: 0.035–0.042; CFI = 0.979; TLI = 0.976; SRMR = 0.035), based on what is stated in the literature [46,47], for this type of analysis.

Figure 2 shows the proposed base model of entrepreneurial intention. Except for the relationship between SN→EI and in the relationship SB→EI, the hypotheses that the PA and PBC have a direct, positive, and significant effect (p < 0.000) on the EI (DIR_PA→EI = 0.757; DIR_PBC→EI = 0.277) and that, at the same time, the SN has a direct, positive, and significant effect (p < 0.000) on PA and PBC (DIR_SN→PA = 0.766; DIR_SN→PBC = 0.562) is confirmed and can be observed in Figure 2. On the same basis, we rejected the hypothesis that SN exerts a direct, positive, and significant effect on EI; instead, the effect of SN on EI was negative (DIR_SN→EI = −0.108) and significant (p < 0.000). In turn, the model finds empirical support for a positive and significant total indirect effect of (p < 0.000) of SB on EI (IND_SB→EI = 0.136), both through SN→PA→EI as well as through SN→PBC→EI, and at the same time, SB has a significant negative indirect effect (p < 0.000) on EI through SN (IND_SB→SN→EI = −0.024).

Similarly, the results of the indirect model [46] show that SN has an indirect, positive, and significant total effect (p < 0.000) on EI (IND_SN→EI = 0.625). More specifically, it has an indirect, positive, and significant effect (p < 0.000) on PA (IND_SN→PA→EI = 0.575) and has an indirect, positive, and significant effect (p < 0.000) on PBC (IND_{SN→PBC→EI} = 0.158). This is consistent with the results of previous studies, in which it has been verified that the main influence of SN on EI is through its effect on PA and PBC.

Table 5. Direct and indirect effects.

Direct and Indirect Effects EI
Factor	Total	Total Indirect	Indirect Effects					Direct
			SB→SN→EI	SB→SN→PA→EI	SB→SN→PBC→EI	SN→PA→EI	SN→PBC→EI
PA	0.757							0.757
PBC	0.277							0.277
SN	0.625	0.733				0.575	0.158	−0.108
SB	0.136	0.136	−0.024	0.125	0.034

Note: Only significant direct and indirect effects are shown.

shows the direct and indirect effects of the variables of the base model on EI.

Finally,

Table 6. Empirical testing of hypotheses.

No.	Relation	Supported
(H1) 1	SB→EI	No
(H2) 2	PA→EI	Yes
(H3) 3	PBC→EI	Yes
(H4) 4	SN→EI	No

summarizes the results of the empirical testing of the hypotheses formulated in this study.

5. Discussion and Conclusions

The objective of this study was to show the relationship between the entrepreneurial intention constructs of the theory of planned behavior (TPB) model and subjective well-being, by developing an empirical model of entrepreneurial intention incorporating the construct of subjective well-being. We consider the findings to be very important in contributing to the development of informed knowledge on sustainable economic growth, where scientific findings on the relationship between personal well-being and entrepreneurial intentions can generate decent jobs, in harmony with the environment and impact the mental and physical health of the population, bringing us closer to the uptake of sustainable entrepreneurship (SDG 8).

The results of this research firstly indicate that the composite reliability indices for the model show adequate values as recommended (ρ between 0.983 and 0.912), above the minimum recommended (ρ > 0.7). The extracted variance indices also show satisfactory values (IVE between 0.919 and 0.567), above the suggested minimum (IVE > 0.5), as shown in Table 4. The goodness-of-fit indices of the measurement model are above the suggested minimum values (χ2 = 788.420; df = 261; p < 0.0000; RMSEA = 0.044, IC90%: 0.041–0.048; CFI = 0.973; TLI = 0.969; SRMR = 0.036).

In relation to the estimation of effect sizes based on R², the results show that the model explains 80.1% of the variance in EI, where PA appears as the most important factor in explaining EI, which is reflected in the values of the standardized regression coefficients, where λPA→EI = 0.757, λPBC→EI = 0.277 and λSN→EI = −0.108. At the same time, the model also shows an important contribution of SN by explaining 57.7% and 32.7% of the variance in PA and PBC respectively. On the other hand, SB explains 4.7% of the variance in the SN. The results obtained in this research can be considered satisfactory in comparison with values reported in previous studies, most of which have been based on multiple linear regression models, which at most have explained around 40% of the variance in EI. [6,42]. Therefore, according to the evidence and results of this study, hypothesis 1 (H1) is rejected, hypothesis 2 (H2) is accepted, hypothesis 3 (H3) is accepted, and hypothesis 4 (H4) is rejected. It is possible to point out that the proposed model presents an adequate explanatory and predictive capacity. There are several meta-analytic studies on the theoretical construct of entrepreneurial intention that coincide with the results of our study with respect to hypothesis 2 (H2), hypothesis (H3), and hypothesis 4 (H4) [66,67,68], which leads to a deepening of the importance of the belief of being able to carry out an entrepreneurial venture, being important to create entrepreneurial ecosystems in our society that strengthen aspects of self-esteem and self-confidence in entrepreneurs. Nevertheless, we highlight the results of hypothesis 1 (H1), which show that there is no direct relationship between subjective well-being and entrepreneurial intention (see Figure 2 and Table 5). This finding allows us to compare with the results found by Zhang et al. [69] which indicates that subjective well-being has a direct relationship with entrepreneurial intention (−0.128) where their n = 275 and R2 = 0.426, in contrast to our results. In our case with n= 1043 and with an explanatory robustness of the model as noted above (R2 = 80.1% of the variance in EI). These results provide the basis for future research exploring the effects of the social environment on well-being and entrepreneurial intention, investigating the valuation of sustainability by the entrepreneur’s immediate environment, since external factors related to the subjective norm (SN), according to our results, mediate between well-being and entrepreneurial intention. This is relevant for our society since the subjective norm (SN) has a mediating effect, which could reinforce public policies and entrepreneurial training a behavior oriented to sustainable well-being.

This leads us to conclude that further studies of the effects of the subjective norm (effect of support networks, family support, and close environment) on entrepreneurial intention are highly recommended. In addition to the above, the present research opens up the possibility of a line of research that allows the incorporation of quality of life and well-being as a main element in different areas the phenomenon of entrepreneurship and society, highlighting training, public policies, and the development of a new approach to entrepreneurship and society. In terms of limitations, the study shows a limited case population, and further research should be conducted to generalize the researchers’ results, although this study was conducted on the basis of urban population and in a single country.

On the other hand, there is an interesting gap to incorporate new studies on the relationship of well-being constructs, such as sustainable well-being, studying its relationship with entrepreneurial intention and entrepreneurial behavior. It is advisable to study entrepreneurial intention in specific populations and to relate studies between populations in order to check whether the constructs behave in the same way, for example, migrant populations [70], entrepreneurial intention in indigenous populations [71], among others. At the same time, it is also important to advance in understanding the relationships of the theoretical constructs between entrepreneurial skills [72,73] their training, and entrepreneurial intention.