Current Issue : April-June Volume : 2022 Issue Number : 2 Articles : 5 Articles
In a world context of transition from a resource-based economy to a knowledge-based economy, marked by the strong progression of information technologies and the fourth industrial revolution, entrepreneurship education and the support to entrepreneurial students are heralded in a very optimistic and voluntary manner at the political, economic, academic and media levels. It is seen as a way to transform individuals, local ecosystems and societies in a manner where entrepreneurship creates value and well-being. In fact, the Global Entrepreneurship Monitor - an international entrepreneurship indicator - considers entrepreneurship education as one of the 9 framework conditions for the evolution of entrepreneurship. Therefore, the development of the entrepreneurial educational ecosystem is believed to influence cultural values and to foster the economic development of a nation. For this reason, the following question is asked: how can education foster entrepreneurship in Morocco? Hence, this paper aims firstly to present some theoretical guidelines on entrepreneurship education; and then through semi-structured interviews, to explore the current state of entrepreneurship in Morocco and the training needed to improve the entrepreneurial educational ecosystem from the perspective of Moroccan entrepreneurs....
In this paper, the IoT-based adaptive mutation PSO-BPNN algorithm is used to conduct in-depth research and analysis of the entrepreneurship evaluation model for college students and practical applications. -is paper details the principle, implementation, and characteristics of each BP algorithm and PSO algorithm. When classifying college students’ entrepreneurship evaluation based on BP neural network, because BP algorithm is a local optimization-seeking algorithm, it is easy to fall into local minima in the training phase of the network and the convergence speed is slow, which leads to the reduction of classifier recognition rate. To address the above problems, this paper proposes the algorithm of PSO optimized BP neural network (PSOBPNN) and establishes a classification and recognition model based on this algorithm for college students’ entrepreneurship evaluation. -e predicted values obtained from the particle swarm optimization neural network model are used to calculate the gray intervals, and the modeling samples are further screened using the gray intervals and the correlation principle, while the hyperspectral particle swarm optimization neural network model of soil organic matter based on the gray intervals is established afterward; and the estimation results are compared and analyzed with those of traditional modeling methods. -e results showed that the coefficient of determination of the gray interval-based particle swarm optimization neural network model was 0.8826, and the average relative error was 3.572%, while the coefficient of determination of the particle swarm optimization neural network model was 0.853, and the average relative error was 4.34%; the average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model were 8.79%, 6.717%, and 9.9%, respectively. -e average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model are 8.79%, 6.717%, and 9.468%, respectively. In general, the entrepreneurial ability of college students is at a good level (83.42 points), among which the entrepreneurial management ability score (84.30 points) and entrepreneurial spirit (84.16 points) are basically the same, while the entrepreneurial technology ability is relatively low (82.76 points), and the evaluation results are further verified by the double case analysis method. -e current problems encountered by university students in entrepreneurship are mainly the lack of practicality, which indicates that universities, industries, and national strategy implementation levels are not sufficiently focused and collaborative in entrepreneurship development to varying degrees....
The policy reforms on disclosure of individual skills and the increasing number of studies focusing on individual attributes of CEOs and directors have motivated research exploring the skill of directors. In this study, we are examining the benefit of director skill and firm performance. This study answers whether the skill generality or skill specialty is beneficial to the firm. We employ the multidimensional category of skill variable for director and CEO on the Taiwan stock market. The empirical result shows that executives and board members with higher educational backgrounds, expertise, and experiences contribute to higher firm performance and lower firm risks. Furthermore, we also find that generalist skill in directors is associated with better firm performance and firm risk....
Generalized Beta-G family of distributions proposed has alternative distributions to unbounded distributions for modeling price returns. In contrast to Gaussian and other unbounded distributions that take values from (−∞,∞) , Generalized Beta-G family of distributions takes values from [0,∞) so as to properly contain only positive valued observations like that of price returns. In line with this, Nine (9) befitting candidates of the Generalized Beta-G family of distributions were proposed and subjected to monthly prices of cereals. Chen distributional random noise outstripped other candidates of the Generalized Beta-G family of distributions to produce minimum monthly standard deviations of 0.2686 (26.86%), 0.2572 (25.72%), 0.2404 (24.40%), 0.2267 (22.67%), 0.2257 (22.57%), 0.2544 (25.44%), 0.2343 (23.43%), 0.2391 (23.91%), 0.2273 (22.73%) and 0.2465 (24.65%) for prices of Rice, Maize, Sorghum, Millet, G-corn, Cowpea, Groundnut, Beans, Wheat and Cassava respectively. Chen and Loglogistic distributional random noises are the leading candidates among the Generalized Beta-G family of distributions in modelling price returns of the cereals, followed by Fréchet, Weibull and Birnbaum- Saunders random noises in order of significant. Lomax and Linear Failure Rate (LFR) are the ineffective random noises in modeling the price returns....
Business incubator plays an important role in cultivating the source enterprises of strategic emerging industries, building an innovation-oriented country, and forming an entrepreneurial upsurge driven by scientific and technological innovation in the whole society. Chinese scholars have conducted abundant academic research in the field of business incubator. Sorting out the research and development of business incubator will be beneficial to further research. This paper summarizes the literature on incubator research from 1999 to 2021 collected in Chinese Social Science Citation Index (CSSCI) database, and analyzes the research status of business incubator and the problems existing in the process of incubator construction and development. It is pointed out that the future construction of business incubator in China can be carried out from the construction of innovation environment, marketization and industrialization, and the promotion of talent mechanism and enterprise system....
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