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Gosztonyi, M.; , . Profiling (Non-)Nascent Entrepreneurs. Encyclopedia. Available online: https://encyclopedia.pub/entry/21258 (accessed on 23 July 2024).
Gosztonyi M,  . Profiling (Non-)Nascent Entrepreneurs. Encyclopedia. Available at: https://encyclopedia.pub/entry/21258. Accessed July 23, 2024.
Gosztonyi, Márton, . "Profiling (Non-)Nascent Entrepreneurs" Encyclopedia, https://encyclopedia.pub/entry/21258 (accessed July 23, 2024).
Gosztonyi, M., & , . (2022, April 01). Profiling (Non-)Nascent Entrepreneurs. In Encyclopedia. https://encyclopedia.pub/entry/21258
Gosztonyi, Márton and . "Profiling (Non-)Nascent Entrepreneurs." Encyclopedia. Web. 01 April, 2022.
Profiling (Non-)Nascent Entrepreneurs
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This entry is adapted from the peer-reviewed paper 10.3390/su14063571

System-level feature sets with four machine learning modeling algorithms: multivariate adaptive regression spline (MARS), support vector machine (SVM), random forest (RF), and AdaBoost.

nascent entrepreneurs machine learning

1. Introduction

The exploration of the factors of entrepreneurial intention is a constantly evolving field of business research. In order to capture the characteristics of nascent entrepreneurs (NEs), researchers agree with Van Stel et al. [1] that analysis of both economic and non-economic factors is essential. Thus, in the researchers' group characteristics analysis, the goal was not only to explore the socio-economic context and individual characteristics and motivations but also to analyze the individual perceptions. Consequently, analytical methods that can handle system dynamism were essential for the analysis.
As a consequence, researchers worked with models based on machine learning for the system-wide analysis of the characteristics of nascent entrepreneurs. The idea that entrepreneurial activities are “complex social problems” that create nonlinear network loop systems and thus depend on dynamic properties that are very difficult to predict is well established in the entrepreneurial literature [2][3][4]. Nascent enterprises are thus complex adaptive systems [5] that incorporate interactive, nonlinear dynamic mechanisms. Machine learning methods offer very useful tools for accurate analysis of systems of such complexity due to their ability to take into account all available data and all possible interactions and nonlinear forms [6][7].

2. Nascent Entrepreneurs

Several studies show that entrepreneurship has a positive effect on economic growth [8][9]. As a result, examining factors influencing entrepreneurship that can contribute to the development of the business ecosystem, which has a positive impact on economic growth and employment, has become an increasingly important area of research in recent years [10].
In the study, nascent entrepreneurs (NEs) were defined based on the definitions of Lueckgen et al. [11] and Wagner [12] as people who (alone or with others) are actively involved in setting up a new business and who expect to become owner(s) or co-owners of this economic entity in the future. The definition of nascent entrepreneur captures a point in the process of becoming an entrepreneur where the entrepreneurial intention and the gestation stage of an individual have been completed, and the entrepreneur is already devoting time and resources to the concrete foundation of his/her business idea [13][14][15]. This stage is followed by the realization of entrepreneurial behavior within a structured framework when the entrepreneurial activity already takes place with the achievement of sales income [16]. Although not all NEs reach the creation of a structured, income-generating enterprise, the existence of an NE is a critical point in the entrepreneurial life cycle; thus the factors influencing it deserve special attention [17].
According to Rotefoss and Kolvereid [18], NE studies can be divided into three categories as follows: research areas in which (1) the individual, the entrepreneur, is the focus, (2) the environmental, regional, or macro characteristics are emphasized in the development of the process, and, finally, (3) the research focuses on the actual activities of the entrepreneurs during the nascent period of the firm. In the analysis, researchers highlighted the first and second categories due to the cross-sectional data.
Demographic and economic factors were initially emphasized in mapping the characteristics of individuals who become entrepreneurs, but research on the impact of these factors on becoming a nascent entrepreneur is highly mixed. According to Delmar and Davidsson [19], Kolvereid [20], and Minniti [21], while the factors influencing entrepreneurship are the same for men and women, men are more likely to become nascent entrepreneurs. In contrast, Capelleras et al. [22] found no significant difference in the likelihood of women and men becoming entrepreneurs in their model, where the effects of human, social, and financial capital were included in the analysis. Mueller [23] hypothesized that the opportunity cost of those with higher incomes is higher and they are reluctant to give up their higher-paying employee work for precarious income from the business, yet their research found that self-employment is more attractive to those with higher incomes. In contrast, Kim et al. [24] found that neither household wealth nor household income increases the chances of becoming a nascent entrepreneur [22][25][26][27].
The results in the literature on the impact of education are not clear either. In the Swedish sample, the educational attainment of nascent entrepreneurs was measured to be higher than in the control group [19], while Capelleras et al. [22] found that those with higher education were less likely to become nascent entrepreneurs. However, in addition to education, the variety of skills acquired plays a greater role in becoming an entrepreneur. The more diverse and colorful the path an individual travels in education and can be characterized by switching between each educational opportunity, or the number of trainings completed, the more likely he or she is to become an entrepreneur [28]. Furthermore, the results of research examining various demographic factors show that nascent entrepreneurs are more prevalent among the younger and middle-aged population [1][17]. The propensity to start a business describes an inverse U-curve within the adult population according to age. Nagy et al. [29], on examining the entrepreneurial profiles of Croatia, Hungary, Romania, and Serbia, concluded that, except in Serbia, where NEs were most common in the 35–44-year-old age group, members of the 25–34-year-old age group were most likely to become entrepreneurs. Alomani at al. [30] found that cognitive abilities influence the nascent entrepreneurship process in two ways. Cognitive capital traits affect the outcome directly and indirectly, through boosting the impacts of human and social capital. Differences in cognitive traits may explain the different levels of success of nascent entrepreneurs with similar human and social capital resources. Cai et al. [31] also highlighted the importance of social capital and entrepreneurship education through entrepreneurial passion and entrepreneurial self-efficacy in nascent entrepreneurial behaviors.
In addition, a great number of studies explore the key role of environmental, socioeconomic, and macroeconomic variables in NEs [18][29][32]. Becoming an entrepreneur is influenced by geographical location, the economic performance of the country, and the local, business-friendly ecosystem [33][34]. Macroeconomic considerations are definitely considered by an individual when starting a business, and he or she chooses an entrepreneurial, self-employed way of life if the financial and non-financial benefits outweigh those of being employed [35][36][37]. However, Mueller [23] emphasized not only the availability of economic capital but also the role of social capital in these decisions.
Kirzner [38][39] highlighted the importance of individual and macro-level perception of opportunity as a fundamental and distinguishing feature of entrepreneurial behavior, which Wagner [12] later identified as particularly important for nascent entrepreneurs. Baciu et al. [40], examining how nascent entrepreneurs’ personal characteristics influence entrepreneurial perceived behavioral control, found that personality traits empathy and adaptive assertiveness equally have a significant effect. Thus, not only individual demographic and economic factors but also aggregate and macro factors, along with perceptual factors, play a prominent role in becoming a nascent entrepreneur. Perceptual factors in the literature consider personal perceptions and judgments about oneself and the environment, which, although subjective, nevertheless play an important role in individuals’ decisions regarding starting a business [25]. These perceptual variables are further broken down in the literature into the individual’s self-perception [41] and the subjective perception of the environment, which constitutes perceptions of the economic and social context [42]. The literature highlights that role models play a particularly important role in the development of perceptual factors [43], which are often based on popular entrepreneurs or out of acquaintance or family [44][45][46][47]. Mueller [23] pointed out that the importance of perceptual factors declines in the life cycle following the NE stage.
A great number of theoretical models have been set up to synthesize the factors that shape the NE. One of the first theories can be linked to Arenius and Minitti [25], who studied several sets of features in a complex way. The authors described the characteristics of NEs through three groups of factors: (1) demographic and economic characteristics: age, gender, education, job status, and household income; (2) perceptual variables: perception of opportunities, confidence in skills and abilities, fear of failure, and knowledge of other entrepreneurs; and (3) aggregate factors: country effect and macro contextual characteristics. Juric et al. [48] used a complex approach to create a profile of Croatian-born entrepreneurs. Using the neural network method, the most important characteristics defining nascent entrepreneurs were identified by attitudes, skills, and demographics. Nguyen [49] used the method of structural equation models to examine the factors influencing the emergence of young people in Generation Y in Vietnam as nascent entrepreneurs. In their model, they synthesized macro variables (entrepreneurial ecosystem, entrepreneurship education), demographic-economic variables (family background), perceptual variables (perceived behavioral control, social evaluation, perceived opportunity, entrepreneurial intention), and attitude variables (entrepreneurial self-efficacy). Shapero and Sokol [50] incorporated the results of research on entrepreneurial intent into a complex model wherein they found that entrepreneurial intent is influenced by the perceptions of personal desirability, feasibility, and propensity to act. Ajzen [51] supplemented Shapero’s model, stating that entrepreneurial intentions depend on personal attractiveness, social norms, and feasibility.
The theory base of the study builds on the model of Arenius and Minitti [25]. Through this theoretical model, researchers approach nascent entrepreneurs in Hungary, supplementing the original model by further breaking down the perceptual variables into the perceptual subcategories as social environment perpetuation and individual’s perpetuation of oneself. Based on the framework of Arenius and Minitti [25], individual, environmental, and perceptual effects can be grasped as well as synthesized. The management and interpretation of a set of variables in a system are made possible by the fact that sociodemographic variables are path-dependent and, consequently, change slowly, as is the case with perceptual variables, as it takes a long time to change the way individuals think about themselves and their role in society; finally, country-specific variables also change slowly over time [25]. All this change in the long run allows researchers to analyze the variables in a common model.

3. Analysis of Nascent Entrepreneurs in Hungary

In the analysis, researchers wanted to give a statistically reliable forecast of the factors that lead to someone becoming a nascent entrepreneur in Hungary based on the data of GEM 2021. They used the R-program Caret package for the analyses [52]. For machine-learning-based models, researchers divided the sample into training data sets containing 80% of the data and test data which were 20% of the sample. During the design of the training and test data sets, researchers used a function that retained the ratio of the predictor categories to the output variable. As the missing values did not exceed 5% for any of the involved variables, they were able to impute missing data using the k-nearest neighboring method. The variables were then normalized to a range of 0 to 1 using the min–max transformation.
To build their models, researchers created descriptive statistics to examine primarily and visually how predictors affect the output variable. If researchers group the predictor variables according to the categories of the output variable, they have the opportunity to review the possible correlation of the variables with density diagrams (Figure 1).
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Figure 1. Density diagrams of the nascent variable along the predictor variables.
These results only serve as a guide to determine approximately which variables would play an important predictive role in the upcoming models. Figure 1 shows that the fact that the respondent wants to start a new business in the future (FutureBuisnessStart), the self-employed labor market position (EmploymentSelf), and the fact that the respondent thinks that society is one where entrepreneurs have high prestige (ContextEntrHigh), thinks that he or she has the knowledge and skills to start a business (skillStart), considers the socio-economic context appropriate for starting a business (EasyStartBisness), and considers himself/herself to be proactive (proAct) and middle-aged (age) appeared to be variables that could become potentially strong predictive variables in the models. Based on this, it is expected that start-up entrepreneurs will be shaped primarily by individual perceptual variables (FutureBuisnessStart, skillStart, proAct) and variables measuring socio-environmental perceptions (EasyStartBisness, ContextEntrHigh), in addition to variables measuring demographic and economic factors (EmploymentSelf, age).
The data were further analyzed by the method of recursive feature elimination (RFE). Researchers used this method because most machine learning algorithms can determine which predictors are important for predicting the output variable; in some cases, however, they can omit variables that are known to be theoretically or practically significant in exploring a particular entity. Based on RFE’s exploratory analysis, the four most important predictors were whether he/she wants to start a new business in the future (futureBusiStart), whether he/she currently has a business (ownerBusiness), he or she is self-employed (employeeSelf), and he or she belongs to the middle-aged age group (age). Thus, the RFE results suggest that demographic-economic factors (ownerBusiness, employeeSelf, age) play a stronger role in the case of nascent entrepreneurs than the descriptive statistics showed before.
After pre-analysis of the data, researchers modeled the data with MARS, AdaBoost, random forest, and svmRadial machine learning algorithms. For each machine learning model, they performed cross-checking and optimal tuning of the hyperparameters in order to increase model performance and to select the optimal models for prediction.
Predictions of the final models generated by the algorithms are provided in the form of a confusion matrix (Table 1).
Table 1. Confusion matrix and statistics.
MARS Reference AdaBoost Reference
No Yes No Yes
Prediction No 351 24 No 354 27
Yes 12 15 Yes 9 12
Random Forest Reference svmRadial Reference
No Yes No Yes
Prediction No 357 27 No 359 30
Yes 6 12 Yes 4 9
The confusion matrix shows the differences between the predictions made in the test data set and the actual data at the item number level. It can be seen from Table 1 that the MARS model gave a false-positive result in 3% and a false-negative result in 6% of the values; consequently, the accuracy of the model predictions was 91.04%. The AdaBoost algorithm gave a false-positive result of 2% and a false-negative result of 7%, and its prediction accuracy was the same as the MARS algorithm, 91.04%. Random forest gave a false-positive result of 1% and a false-negative result of 7%, resulting in a model with a predictive accuracy of 91.59%. Finally, svmRadial gave 1% false-positive and 7% false-negative results, resulting in a model prediction accuracy of 91.74%.
It can be seen from all this that all algorithms were able to predict nascent entrepreneurs above 90%, based on predictor variables. There is no significant difference in the prediction accuracy of the four algorithms; however, with a few tenths of a percentage point, svmRadial performed the best. If researchers assign kappa values to the forecast values, the performances of the models are as in Figure 2.
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Figure 2. Accuracy and kappa values of the models (in Figure 2, the MARS algorithm is denoted by the term “earth” due to copyright issues).
Based on Figure 2, it can be seen that the average kappa values ranged from 0.00461 to 0.39172. For kappa values as well as for predictions, svmRadial performed the most reliably (κ 0.39172) and AdaBoost the least reliably (κ 0.00461). It is also important to note that there are quite large differences in kappa values between the different algorithms. While SVM, MARS, and RF gave low but nearly similar kappa values, AdaBoost’s kappa values lag far behind other models.
By further examining the performance of the models along ROC, sensitivity, and specification, Table 2 is obtained.
Table 2. ROC, sensitivity, and specificity values of machine learning models.
  Average of ROC StdDev of ROC Average of Sens StdDev of Sens Average of Spec StdDev of Spec
AdaBoost 0.948 0.001 0.959 0.005 0.437 0.036
MARS 0.956 0.003 0.973 0.005 0.427 0.056
Random Forest 0.953 0.002 0.965 0.017 0.415 0.169
svmRadial 0.941 0.010 0.967 0.002 0.413 0.088
Total 0.949 0.010 0.968 0.008 0.420 0.087
Based on the ROC values, MARS (0.956 ± 0.003) and random forest (0.953 ± 0.002) algorithms performed best. For the sensitivity values, MARS (0.973 ± 0.005) and svmRadial (0.967 ± 0.002) performed the most accurately, while for the specificity values, the AdaBoost (0.437 ± 0.036) and MARS (0.427 ± 0.056) algorithms proved to be the most accurate.
Consequently, it is difficult to determine one specific algorithm that performed best. Each algorithm was able to predict NEs with very high accuracy (>90%). However, the two nonlinear classification models stood out among the predictors. Therefore, it can be said that svmRadial was the most accurate predictor of nascent entrepreneurs; however, the MARS model seems to be a better performing model if researchers include metrics based on false-positive and false-negative values in the validation criteria.
Consequently, in identifying the predictive variables of the models, all four models were included in the further analysis. This decision was made not only on the basis of predictions metrics but also from a theoretical point of view, as the four different machine learning models showed different predictive variable importance. Variable importance reflects the relative contribution of each predictor to the optimal forecasting model. The higher this value, the greater the significance of the variable.
In the case of the MARS model, the fact that the respondent currently owns a business (ownerBusiness), wants to start a new business in the future (futureBusiStart), belongs to the middle-aged group (age), is self-employed (employeeSelf), has stopped a business in the past (stopBuisness), knows entrepreneurs personally (knowEntreprenure), considers himself/herself as a proactive person (proAct), and has a medium-sized household (hhsize) were the variables that made nascent entrepreneurs predictable (Figure 3).
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Figure 3. Importance of predictive variables of MARS model.
However, while the MARS model identified eight predictor variables for its prediction, the svmRadial model included all 30 variables in the analysis, albeit with very different intensities (Figure 4).
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Figure 4. Importance of predictive variables for the svmRadial mode.
Based on the SVM model, the three most important predictor variables were: the respondent currently has a business (ownerBuisness), the respondent is self-employed (employeeSelf), and the respondent feels that he or she has the knowledge to start a business (skillStart). However, similar to the MARS algorithm, the fact that the respondent knows an entrepreneur (knowEntreprenure) appears as an important factor among the predictor variables of the model. Unlike MARS, the SVM identified demographic-economic variables (age, hhinc) with medium importance. Furthermore, the aggregate variables were included in the model with low predictive strength. It is also important to note that while in the MARS model the stopBuisness variable played a very important role, the SVM algorithm listed the importance of the variable in the last place.
The AdaBoost model gave almost exactly the same results in terms of the importance of variables as svmRadial (Figure 5). Consequently, the algorithm identified demographic and economic factors as the most important predictor variables, followed by variables measuring an individual’s self-perception, followed by aggregate variables and then socio-environmental perception variables.
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Figure 5. Importance of predictive variables for the AdaBoost model.
The random forest algorithm formed a different model from the previous three models based on the importance of the variables (Figure 6). However, like the other algorithms, RF also emphasizes demographic factors (ownerBuisness, age, employeeSelf) in the top three predictors; it also handles the social-environment perceptual variable (socContextBusiSoc) and the aggregate variable (HuRegion) in a leading position in the model.
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Figure 6. Importance of predictive variables for the RF model.
Consequently, the models formed by the four artificial intelligences yielded different results in the order of importance of the variables (Table 3).
Table 3. Selection of the top ten predictor variables along the models.
Context Variable Code MARS svmRadial AdaBoost RandomForest
Aggregate Conditions HuRegion       X
settlementType        
Demographic and Economic Factors Age X     X
Gender        
education        
hhsize X     X
work        
hhInc        
employeeFull   X X  
employeePart        
employeeSelf X X X X
Intrapreneur        
ownerBusiness X X X X
ownerBusinessPart        
stopBusiness X      
Perceptual Variables Social socContextEqualInc        
socContextEntrNotCareer        
socContextEntrHighStatus        
socContextMediaGEntr        
socContextBuisSoc       X
Individual futureBusiStart X X X X
knowEntrepreneur X X X  
opportStart       X
skillStart   X X X
fearfail   X X  
easyStartBusin   X X  
rareOpportunities       X
proAct X X X  
creativ        
visionCareer   X X  
As shown in Table 3, several variables can be identified that each model considers important predictors. Among these demographic and economic factors, the respondent has a business (ownerBusiness) and the respondent is self-employed (employeeSelf). Among the perceptual variables, in the individual segment, researchers find a variable (futureBusiStart) that each model treats as an important predictor variable.
The models can be divided into roughly two patterns, with the results of MARS and RF as one pattern and the results of SVM and AdaBoost as the other. However, it is important to note that while the results of the SVM and AdaBoost models show a perfect agreement for the key predictors, the RF model, unlike MARS, assigns an important role to both aggregate and socio-environmental perceptual variables in its model. Thus, if researchers further examine the results in the order of importance of theoretical categories, they find that each model’s results show the role of demographic and economic variables as the most important factor, followed by the individual’s self-perception (Table 4). These variables are followed by the individual’s perception of society and its environment and then followed by aggregate variables. However, it is important to note that in the case of the MARS model, the latter two variable categories do not play a role.
Table 4. Importance of predictive variable sets in models.
  MARS svmRadial AdaBoost RandomForest
Demographic and Economic Factors 1 1 1 1
Perceptual Variables—Individual 2 2 2 2
Perceptual Variables—Social 0 3 3 3
Aggregate Conditions 0 4 4 4
All this means that each model based on artificial intelligence assumes nascent entrepreneurs as a combined effect of a system in which almost every set of variable features plays an active role. Based on the models, the process of becoming a nascent entrepreneur in Hungary in 2021 is created from the interaction of these feature sets. However, based on the results, it can also be seen that there is some difference between these variable sets. The two main sets of characteristics in NEs are demographic and economic variables (owning a business, age, work status, household size) and individuals’ perceptions of themselves (commitment to start a business, knowledge and experience of starting a business, knowing an entrepreneur personally, and proactive personality). This is followed by the variables measuring the perception of the socio-environment and then the order ends with the aggregate macro variables.

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Subjects: Archaeology
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Márton Gosztonyi
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