Bayesian hierarchical analyses for entrepreneurial intention of students

Abstract

In recent years, entrepreneurship has become an important issue due to national economic development and the contribution of society. Data with a hierarchical structure received more attention and occur frequently in social science, public health and epidemiological researches. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. Traditional logistic regression is inappropriate when data are hierarchically structured. Therefore, this study presents multi-level Bayesian logistic analysis for entrepreneurial intention of students using classical and Bayesian approach. The descriptive result revealed that about 57.4% of the students had an entrepreneurial intention while 42.6% do not have an intention. The results also showed that entrepreneurial education/training and entrepreneurial attitudes significantly predicts students' entrepreneurial intention at 5% level of significance. The model results indicate that the effects of the selected variable on entrepreneurial intention vary across university. By failing to take into account the clustering within university (level 2), Bayesian multilevel effects are not taken into consideration in modeling, the β coefficients in multilevel logistic model using classical approach are distorted somewhat in both directions either in over or under direction. This study also evaluates and compares the behavior of maximum likelihood and Bayesian estimators to investigate the relationship between covariates and the response. Both point and interval estimation performances were investigated. The results revealed that lower standard errors of the estimated coefficients in the Bayesian logistic regression approach as compared to classical approach. Moreover, the results revealed that the length of the Bayesian credible interval is smaller than the length of the maximum likelihood confidence interval for all factors. In order to identify the most plausible method between Bayesian method and maximum likelihood estimation of the data, AIC, BIC and DIC are adopted in this paper. The result of the study depicts that the Bayesian method performs better and more efficient than maximum likelihood estimation. The study recommends that the government as well as the universities should design programs that facilitate entrepreneurship to change the mindset, attitude, and intention of those students who do not have knowhow about entrepreneurship as a future career.

The study of the causal relationship between innovativeness and entrepreneurial intention among undergraduate students

“…This is because those two indicators demonstrate commitment and intention to become an entrepreneur, meaning they are ready to do anything in order to achieve their goal. The entrepreneurial intention among undergraduate students can be created through entrepreneurial education and training (Ayalew, 2020). Therefore, these five indicators should be incorporated in the curriculum of entrepreneurial education.…”

Do entrepreneurial characteristics and education influence start-up potentials among undergraduates? Evidence from a developing country

“…Further-more, Botha et al (2007) found that the special women entrepreneurial programme (WEP) was effective and positively influenced business start-up and venture creation among women in South Africa. Several authors have shown other factors as influencing entrepreneurial propensity among youths to include: entrepreneurial education (Hahn et al, 2020;Ayalew, 2020), supportive environment (Walter & Block, 2016), and parents' entrepreneurial role (Liñán & Fayolle, 2015;Olofinyehun, Adelowo & Egbetokun, 2018;Hopp et al, 2019;Moussa and Kerkeni, 2021). Among the Hungarian students, Gubik (2021) found that positive entrepreneurial attitudes and better knowledge about entrepreneurship process has the chance to positively influence them towards entrepreneurship career.…”

A Risk Assessment Algorithm for College Student Entrepreneurship Based on Big Data Analysis

“…e arrival of the big data era provides college students with such data leasing, data analysis, decision-making outsourcing, data trading, and other entrepreneurial directions. In the era of big data, the huge amount of information has a significant impact on the decisionmaking and development of every enterprise and no enterprise can stay out of the matter [7]. Big data can promote college students' understanding of future demand development trends, and it is the opportunity for college students to choose the direction of entrepreneurship, which enhances the correctness and guarantee of entrepreneurial path selection [8].…”