It is accepted in the literature on entrepreneurship at the regional level that entrepreneurs are the critical element in the process of creating a new venture. This is why entrepreneurship requires entrepreneurial behaviour (intention and action) [1,2,30]. In turn, it has been found that entrepreneurial behaviour depends on contextual variables and personal variables, the latter having been more studied [37,38].

Their importance is very high regarding contextual variables considering that entrepreneurial behaviour develops in a specific context, although some contextual variables are more favourable than others [39,40]. Contextual variables are often classified in the literature as formal institutional variables and informal institutional variables. From a formal institutional perspective, the importance of government policies and programmes, as well as infrastructures (e.g., physical, commercial, technological and financial), market development and innovation transfer is highlighted [41,42]. The role of the education system has also been emphasized in the literature because it enables the development of entrepreneurial vocation, values related to self-employment, skills to create a business and entrepreneurial intention [42,43,44]. Regarding the informal institutional variables that influence entrepreneurial behaviour, the literature underlines the role of culture, which develops over time through the adoption and internalisation, usually unconsciously, of norms, beliefs, practices and customs [45]. In particular, it has been found that cultural diversity can help to explain regional differences in entrepreneurship, as the decision to start a new venture depends on the cultural context in which it takes place [46,47]. Individualistic cultures have also been found to be more conducive to entrepreneurship than collectivistic cultures, as, in the former, people consider their interests before those of the group [48,49].

Regarding the personal variables linked to entrepreneurial behaviour, the literature highlights entrepreneurial intention, which is the variable that best predicts the behaviour of creating a new company at the regional level [50,51]. For these reasons, intention is the central variable in most studies on entrepreneurial behaviour, most of which aim to design causal models to predict it. The remaining personal variables, and occasionally some contextual variables, constitute the independent variables in such causal models [52,53,54]. Regarding its definition, entrepreneurial intention is conceived as an entrepreneur’s propensity to make an intentional, deliberate and planned decision to create a new venture [53,55].

Two of the most widely used models in the literature to predict entrepreneurial intention are Shapero and Sokol’s [56] entrepreneurial event model (EEM) and Ajzen’s [57] theory of planned behaviour (TPB) [20,58]. In the model by Shapero and Sokol, entrepreneurial intention depends on three variables: perceived desirability, which derives from a perceived reasonable business opportunity; perceived feasibility, based on the perception of one’s capabilities; and propensity to act [59,60]. However, it is the TPB model that predominates in the literature due to its higher predictive power [61,62]. The TPB model’s intention to start a new venture depends on the attitude towards entrepreneurship, perceived control and subjective norm [53,63]. Attitude corresponds to desirability, included in the EEM model, and is defined as the personal valuation about being an entrepreneur [53,63]. Regarding perceived behavioural control, this is a variable similar to perceived feasibility in the Shapero and Sokol model and is defined as an individual’s assessment of the extent to which they can perform a specific behaviour [53,63,64]. Perceived control is a construct closely related to self-efficacy, defined as the belief in resources and competencies to achieve one’s goals [65,66]. Finally, subjective norms refer to how the subject himself perceives that his behaviour will be accepted by his belonging or reference group [53,62]. The influence of subjective norms is similar to the process of entrepreneurial role adoption by the subject that she or he observes in a known person or a family member, which largely explains the succession process [67,68].

Despite the many studies conducted and the findings obtained, the literature points out the limitations of existing causal models in predicting complex entrepreneurial behaviour in a regional context. It is mainly due to the small number of variables they incorporate [69,70]. On the other hand, the authors of predictive behavioural studies need quality traditional data sources for a wide range of variables and countries [30]. In order to respond to these demands, several authors suggest enriching predictive models by incorporating new variables and new relationships, as well as variables that, together with intention, reflect effective entrepreneurial behaviour [71]. To study more deeply the quality of data from institutional data sources has also been suggested [71,72,73]. It is precisely in this context that the possibility of designing causal models using data from GEM reports has been raised in the literature, once their theoretical foundation and statistical significance has been confirmed [74,75,76].

According to the GEM, the decision to start a new business depends on a broad set of personal and contextual variables, all of which are equally important[5]. Despite their equal importance, in GEM reports, contextual variables can influence personal variables, although GEM does not specify the nature of this relationship [28,77]. Moreover, as mentioned above, in GEM reports, entrepreneurial intention is just another personal variable [5,30].

The contextual variables are considered an essential part of business creation and directly influence entrepreneurial opportunities, competencies, and preferences. The source of information on these variables is the National Experts Survey, a standard GEM methodology to capture expert judgments to evaluate regional variables [5,30]. Regarding their measurement, the values of the contextual variables represent the average scalar data per country that come from the responses of entrepreneurship experts to specially designed questionnaires. Regarding personal variables, the information is obtained by GEM through the design and application of the Adult Population Survey (APS), a unique and comprehensive questionnaire administered to a minimum of 2000 adults in each GEM country. All personal variables included in GEM reports refer to average percentages of the population aged 18–64 by country and are based on the perceptions and statements of the population [5,30].

Finally, it should be noted that GEM reports also provide several outcome variables or indicators directly or indirectly related to entrepreneurship. This study has included the TEA (Total Early stage Entrepreneurial Activity) indicator, the most critical outcome indicator in the GEM reports [5]. GEM defines TEA as the percentage of the population aged 18–64 who are either a nascent entrepreneur or an owner-manager of a new business. Its inclusion in this study responds to suggestions by several authors to include in causal models, along with entrepreneurial intention, some variable that reflects the “action” inherent to entrepreneurial behaviour [28,29]. Moreover, TEA will be the variable used in this study to achieve objective 4.

Data were obtained from the Global Entrepreneurship Monitor platform (www.gemconsortium.org, accessed on 23 June 2021), similar to other authors [30,138]. Specifically, the information included in the databases available in Excel for 2020 werew used, which is the latest data available on the GEM website when this study was carried out (www.gemconsortium.org, accessed on 25 June 2021). The 45 countries (N = 45) for which data were available for all variables included in this study were selected. It should be noted that the effort and rigour of data collection for GEM that has occurred every year mean this dataset has received a significant amount of respect across the social sciences [138,139]. Finally, the high validity, reliability and unidimensionality of the GEM data have recently been confirmed through the Rasch mathematical model [30].

First, the observed personal and contextual variables used in this study were included in the latest GEM report 2020–2021 (www.gemconsortium.org, accessed on 25 June 2021), and are explained above. In addition, the TEA (Total early stage Entrepreneurial Activity) indicator was selected as an outcome variable. Second, to identify the latent variables or constructs, an exploratory factor analysis was carried out. This analysis was carried out using the principal components extraction method and the Varimax rotation with Kaiser normalization, as performed by other authors [20]. After the analyses, eight latent variables (factors) were obtained. The sum of the squared loads of the rotation explained 87.28% of the variance accumulated through the eight resulting factors. These variables were Education and Culture (ECU), Basic Infrastructures (BIN), Government Policies and Programmes (GPP), Perceptions (PER), Subjective Norm (SUN), Personal Values (PVA), Self-efficacy (SEF) and Entrepreneurial Intention and Action (EIA). Two observed variables were eliminated through this factor analysis: Fear of Failure (personal variable) and Internal Market Dynamics (Contextual variable). Additionally, the reliability changed from 0.86 to 0.89 by eliminating these variables. Only two items in five latent variables were accepted because these items had a low correlation with other latent variables, and the correlation between the items belonging to the same variable was greater than 70% [140].

Firstly, data were examined using the SPSS-25 programme to obtain descriptive indicators. The Partial Least Squares Structural Equation Modelling (PLS-SEM) method was applied through SmartPLS-3 software (3.3.2 version) to minimize the residual variances of the endogenous variable. PLS was selected due to its potential to explain the theory and predict human behaviour [141]. In addition, PLS allows the use of a wide range of sample sizes and reflective variables, and it shows more reliable results and does not require a normal distribution of data [141].

The PLS-SEM approach implies studying the measurement model and the study of the structural model. The Measurement and Structural models take the general form as follows:

where:

• X is a J by 1 vector of indicators;
• Y is a P by 1 vector of latent variables;
• C is a P by J matrix of loadings;
• B is a P by P matrix of path coefficients;
• ε is a J by 1 vector of the residuals of indicators;
• ζ is a P by 1 vector of the residuals of latent variables.

The reflective indicators in the latest GEM report 2020–2021 (www.gemconsortium.org, accessed on 25 June 2021) were used in the study. These are reflective indicators because they meet the criteria proposed by Jarvis, MacKenzie and Podsakoff [142]. As proposed by other authors, an Importance-Performance Analysis (IPMA) and Multigroup Technique were included in this study to achieve objectives 3 and 4, respectively [20,143]. Finally, it should be noted that the ultimate guidelines in applying PLS-SEM in research were followed in the application of PLS-SEM [141,144].