Influence of the Cognition of Student Entrepreneurs on Decision Making Based on Factor Analysis

Shaoying Wang, Linghui Liu, Shaoyu Wang

Abstract


With the progress of science and technology, the emergence of new technologies has greatly promoted young college students' entrepreneurial boom. However, the progress of science and technology not only brings more opportunities for entrepreneurship, but also makes the market competition more intense. High-speed information updating makes the risk of entrepreneurship sharply increase. Individual entrepreneurship gradually transforms into group entrepreneurship, further increasing the complexity of decision-making. In order to reduce entrepreneurial decision-making errors, this study empirically analyzed the impact of entrepreneurial cognitive ability on entrepreneurial decision-making of entrepreneurial groups of students in Beiijing, Shanghai, Guangzhou and other regions who have been entrepreneurs for three months to six years. The data of five factors, entrepreneurial consensus ability, professional allocation ability, monitoring and control ability, decision-making speed and effect of decision making, were collected through questionnaire. The relationships between the factors were initially determined using SPSS, and the causal relationship was further analyzed using multi-variable regression analysis. The results showed that entrepreneurial consensus ability and professional allocation ability had a significant positive impact on decision-making speed, monitoring and control ability had no significant impact on decision-making speed, professional allocation ability and monitoring ability had a significant positive impact on decision-making effect, and entrepreneurial consensus ability had no significant impact on decision-making effect. In conclusion, entrepreneurial cognitive ability of student entrepreneurs has a significant impact on entrepreneurial decision-making.

Keywords


Entrepreneurial cognitive ability; speed of decision making; effect of decision making; multi-variable regression analysis

Full Text:

PDF


Copyright (c) 2019 Shaoying Wang, Linghui Liu, Shaoyu Wang


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
Creative Commons License
Indexing:
Scopus logo Clarivate Analyatics ESCI logo EI Compendex logo IET Inspec logo DOAJ logo DBLP logo Learntechlib logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo