Combining PCA analysis and neural networks in modelling entrepreneurial intentions of students (original) (raw)
Related papers
2013
Despite increased interest in the entrepreneurial intentions and career choices of young adults, reliable prediction models are yet to be developed. Two nonparametric methods were used in this paper to model entrepreneurial intentions: principal component analysis (PCA) and artificial neural networks (ANNs). PCA was used to perform feature extraction in the first stage of modelling, while artificial neural networks were used to classify students according to their entrepreneurial intentions in the second stage. Four modelling strategies were tested in order to find the most efficient model. Dataset was collected in an international survey on entrepreneurship self-efficacy and identity. Variables describe students’ demographics, education, attitudes, social and cultural norms, self-efficacy and other characteristics. The research reveals benefits from the combination of the PCA and ANNs in modeling entrepreneurial intentions, and provides some ideas for further research.
Croatian Operational Research Review, 2010
Entrepreneurial intentions of students are important to recognize during the study in order to provide those students with educational background that will support such intentions and lead them to successful entrepreneurship after the study. The paper aims to develop a model that will classify students according to their entrepreneurial intentions by benchmarking three machine learning classifiers: neural networks, decision trees, and support vector machines. A survey was conducted at a Croatian university including a sample of students at the first year of study. Input variables described students’ demographics, importance of business objectives, perception of entrepreneurial carrier, and entrepreneurial predispositions. Due to a large dimension of input space, a feature selection method was used in the pre-processing stage. For comparison reasons, all tested models were validated on the same out-of-sample dataset, and a cross-validation procedure for testing generalization ability...
Introduction: Studies of the socio-economic function of entrepreneurship have emphasized the critical role that entrepreneurial competence and its implementation play at different stages of the education system. In this paper, we seek to determine the entrepreneurial interest of Spanish youth aged between 15 to 18 years of age, who find themselves in the state-regulated education system, at an initial stage in the development of entrepreneurship. A previously validated ad hoc questionnaire was applied through simple random sampling to 1,764 students at a secondary school in Spain. The analysis is done with Artificial Neural Networks (ANN), a technique that reduces the high dimensionality of data through Cooperative Maximum Likelihood Hebbian Learning (CMLHL), applying neurocomputational methods to the educational sciences. Spanish youth expressed a medium level of interest in entrepreneurship. Analysis with ANN shows that education in entrepreneurial competence is an influential asp...
International Journal of Aquatic Science, 2021
Entrepreneurs have a larger role in an economy's growth and development. Students are a key source of nasant entrepreneurship in developing nations like India, therefore they are encouraged to start their own businesses. As a result, it's important to figure out what drives students to pursue entrepreneurship. The purpose of this study is to find out which personality characteristics help college students in Andhra Pradesh become entrepreneurs. Five personality characteristics were investigated in order to determine the relevant variables, and only one was shown to have a substantial positive impact on entrepreneurial inclination. Perceived attractiveness has a favourable impact on entrepreneurial desire. As a result, perceived desirability is a significant predictor of college students' entrepreneurial intentions. It implies that students who have a strong desire to be their own boss, take advantage of chances, and establish their own job are more likely to become entrepreneurs. This all work is analysing through ResNet Deep leatning technology. At final comparing results such as sensitivity, accuracy and F 1 score these are most improved compared to old techniques.
Profiling nascent entrepreneurs in Croatia - neural network approach
2019
A significant body of research has been conducted to identify the most important characteristics of nascent entrepreneurs. The aim of this paper is to create a model for recognizing nascent entrepreneurs in Croatia, using the Global Entrepreneurship Monitor (GEM) data for 2014. In this research, the artificial neural networks were used as a machine learning method which enabled the recognition of nascent entrepreneurs, as well as the selection of most important variables and profiling. The suggested model includes variables that describe examinees’ attitudes, skills and demographic characteristics, while the binary output variable identifies a nascent entrepreneur. In addition to testing the accuracy of the suggested model, the contribution of this paper lies in the profiling of nascent entrepreneurs in Croatia. This model could be a valuable tool for the government and entrepreneurship support institutions in creating policies and programmes based on recognizing the most important ...
Development Southern Africa, 2020
Previous work on the relationships between entrepreneurship education, prior entrepreneurial exposure and entrepreneurial action has resulted in mixed findings. However, this work typically relies on linear models which may not adequately account for the relationships. Therefore, we explore artificial neural networks (ANN) to test non-linear relationships and compare these results with a linear regression model to understand the previous mixed findings. Data from 125 entrepreneurship graduates in Zambia revealed that a non-linear model best explained the variation in entrepreneurial action, whereby the relationship was cubic. These results explain some of the previously mixed findings and demonstrate the importance of educators, policy makers and scholars paying attention to nonlinear relationships when aiming to promote and further understand entrepreneurship. Therefore, this paper has implications for educational initiatives aiming to augment entrepreneurship education, while also contributing to the development of theory explicating the relationship between entrepreneurial exposure, education and action.
Entrepreneurial Competence: Using Machine Learning to Classify Entrepreneurs
2021
Competencies are behaviors that some people master better than others, which makes them more effective in a given situation. Considering that entrepreneurship translates into behaviors, the competency-based approach expresses attributes necessary in the generation of such behaviors with greater precision. By virtue of the dynamic and complicated nature of entrepreneurial phenomena and, especially, of the numerous data sets and variables that accompany the entrepreneur, it has become increasingly difficult to characterize it. In this study, we use predictive analysis from the machine learning approach (unsupervised learning) in order to determine if the individual is an entrepreneur, based on measures of 20 attributes of entrepreneurial competence relative to classification and ranking. We investigated this relationship using a sample of 6649 individuals from the Latin American context and a series of algorithms that include the following: logistic regression, principal component ana...
Management : Journal of Contemporary Management Issues, 2015
1. INTRODUCTIONPrevious research has confirmed the importance of various personal predispositions for recruitment into entrepreneurship. These predispositions, called entrepreneurial tendencies, inclinations or abilities include a wide spectrum of psychological constructs - from motivational characteristics (e.g. achievement motive, independence motive), specific cognitions (e.g. opportunity identification), specific and general traits (e.g. risk taking, ambiguity tolerance) to abilities (e.g. creativity) (Ahmetoglu and Chamorro-Premuzic, 2010; Caird, 1988; Chell, 2008; Miljkovic Krecar, 2008; Zhao and Seibert, 2006).Correlations of different entrepreneurial characteristics with entreprene-urial intentions and behavior were found to be moderate (e.g. the multiple R between the BIG5 personality dimensions and entrepreneurial status found in Zhao Seibert and Lumpkins' (2010) meta-analysis was R=0.37). Therefore, some researchers investigated the indirect effect of entrepreneurial ...
Entrepreneurship Intention Prediction using Decision Tree and Support Vector Machine
This study discusses the prediction model of entrepreneurship intent for alumni. The data is obtained from the database of an online job market, alumni tracer and survey results to the alumni. This research applies the C4.5 decision tree algorithm to get a prediction model that shows the intention of entrepreneurship. Some essential indicators include Self-efficacy, Need for Achievement, Advisory Quotient, Locus of Control and Passion. The predictive model found that the best predictor was Self-efficacy which contributed to influence the entrepreneurship intention with a value of 79.7 percent. The authors recommend to educational institutions to foster candidate interest through curriculum improvement, field practice or learning models in and out of the classroom.
Mathematics, 2021
Youth unemployment rates present an issue both in developing and developed countries. The importance of analyzing entrepreneurial activities comes from their significant role in economic development and economic growth. In this study, a 10-year research was conducted. The dataset included 5670 participants—students from Serbia. The main goal of the study is to attempt to predict entrepreneurial intentions among the Serbian youth by analyzing demographics characteristics, close social environment, attitudes, awareness of incentive means, and environment assessment as potential influencing factors. The data analysis included Chi-square, Welch’s t-test, z-test, linear regression, binary logistic regression, ARIMA (Autoregressive Integrated Moving Average) regression, and a QUEST (Quick, Unbiased, Efficient, Statistical Tree) classification tree algorithm. The results are interesting and indicate that entrepreneurial intentions can be partially predicted using the dataset in this curren...