Volume 10, Issue 19 (8-2023)
jea 2023, 10(19): 107-117 |
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Siah Siahsarani Kojuri M A. (2023). Presenting and Explaining the Predictive Model of Students' Entrepreneurial Future with an Emphasis on Business Intelligence Indicators. jea. 10(19), 107-117. doi:10.61186/jea.10.19.107
URL: http://jea.sanru.ac.ir/article-1-349-en.html
URL: http://jea.sanru.ac.ir/article-1-349-en.html
Department of Management and Economics, Faculty of Humanities and Social Sciences, Golestan University, Gorgan, Iran.
Abstract: (1154 Views)
Extended Abstract
Introduction and Objective: The issue of employment and guiding the population of university graduates towards entrepreneurship has been and is one of the most challenging and favorite topics of researchers in this field. One of the basic questions in this field is why, in a relatively similar situation and context, some university graduates identify and operate entrepreneurial opportunities, while others do not have such ability? The answer to this multifaceted question can be investigated from various dimensions, one aspect of which, business intelligence indicators, has been considered in shaping the entrepreneurial future.
Material and Methods: In the first step, by using the literature review and research background in the field of entrepreneurship and its relationship with business intelligence, 13 dimensions of business intelligence that can be effective in shaping the entrepreneurial future were identified. In the second step, quantitative methods in data mining (descriptive and predictive) were used to analyze the statistical population. To describe the research community, 401 students of Gorgan University of Agriculture and Natural Resources in 1401 were grouped and analyzed in three separate clusters using the Davis-Bouldin index. Also, artificial neural networks used to design a predictive model of students' entrepreneurial future.
Results: The results of the research showed that in the statistical population of the research, there is a significant difference between students in terms of having business intelligence so based on business intelligence indicators, students can be separated and differentiated into three clusters. The results in the modeling stage of predicting the entrepreneurial future of students using neural networks showed that business intelligence indicators have a high predictive ability and by using them, changes in the dependent variable can be predicted with an accuracy of 0.925. Also, the results of the sensitivity analysis of the importance of demographic variables confirmed the impact of these variables on shaping the entrepreneurial future.
Conclusion: According to the results of the current research, it can be concluded that students are in a different situation from each other in terms of business intelligence indicators, which can play an important role in shaping their entrepreneurial future. Therefore, it is suggested that business intelligence indicators should be strengthened and improved through skill training courses, considering business intelligence indicators in the stages of student selection and selection, providing practical courses in the university, strengthening the relationship between industry and university, and introducing successful entrepreneurs to students.
Introduction and Objective: The issue of employment and guiding the population of university graduates towards entrepreneurship has been and is one of the most challenging and favorite topics of researchers in this field. One of the basic questions in this field is why, in a relatively similar situation and context, some university graduates identify and operate entrepreneurial opportunities, while others do not have such ability? The answer to this multifaceted question can be investigated from various dimensions, one aspect of which, business intelligence indicators, has been considered in shaping the entrepreneurial future.
Material and Methods: In the first step, by using the literature review and research background in the field of entrepreneurship and its relationship with business intelligence, 13 dimensions of business intelligence that can be effective in shaping the entrepreneurial future were identified. In the second step, quantitative methods in data mining (descriptive and predictive) were used to analyze the statistical population. To describe the research community, 401 students of Gorgan University of Agriculture and Natural Resources in 1401 were grouped and analyzed in three separate clusters using the Davis-Bouldin index. Also, artificial neural networks used to design a predictive model of students' entrepreneurial future.
Results: The results of the research showed that in the statistical population of the research, there is a significant difference between students in terms of having business intelligence so based on business intelligence indicators, students can be separated and differentiated into three clusters. The results in the modeling stage of predicting the entrepreneurial future of students using neural networks showed that business intelligence indicators have a high predictive ability and by using them, changes in the dependent variable can be predicted with an accuracy of 0.925. Also, the results of the sensitivity analysis of the importance of demographic variables confirmed the impact of these variables on shaping the entrepreneurial future.
Conclusion: According to the results of the current research, it can be concluded that students are in a different situation from each other in terms of business intelligence indicators, which can play an important role in shaping their entrepreneurial future. Therefore, it is suggested that business intelligence indicators should be strengthened and improved through skill training courses, considering business intelligence indicators in the stages of student selection and selection, providing practical courses in the university, strengthening the relationship between industry and university, and introducing successful entrepreneurs to students.
Keywords: Artificial neural network, Business, Clustering, Entrepreneurship, Market intelligence
Type of Study: Research |
Subject:
کارآفرینی در کشاورزی
Received: 2023/01/13 | Accepted: 2023/06/24 | Published: 2023/09/18
Received: 2023/01/13 | Accepted: 2023/06/24 | Published: 2023/09/18
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