Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index

So Young Sohn; Ann Sung Lee

doi:10.1016/j.eswa.2013.01.009

Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index

So Young Sohn, Ann Sung Lee

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

16 Citations (Scopus)

Abstract

Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.

Original language	English
Pages (from-to)	4003-4009
Number of pages	7
Journal	Expert Systems with Applications
Volume	40
Issue number	10
DOIs	https://doi.org/10.1016/j.eswa.2013.01.009
Publication status	Published - 2013 Aug

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government ( NRF-2011-327-B00324 ). We also appreciate Yong Han Ju for his comments on our work. Permission to use excerpts/tables from Global Entrepreneurship Monitor: 2008 and 2009 Executive Reports, which appear here, has been granted by the copyright holders. The GEM is an international consortium and this report was produced from data collected in, and received from, 22 countries in 2008 and 18 countries in 2009. Our thanks go to the authors, national teams, researchers, funding bodies and other contributors who have made this possible.

All Science Journal Classification (ASJC) codes

Engineering(all)
Computer Science Applications
Artificial Intelligence

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10.1016/j.eswa.2013.01.009

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title = "Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index",

abstract = "Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.",

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note = "Funding Information: This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government ( NRF-2011-327-B00324 ). We also appreciate Yong Han Ju for his comments on our work. Permission to use excerpts/tables from Global Entrepreneurship Monitor: 2008 and 2009 Executive Reports, which appear here, has been granted by the copyright holders. The GEM is an international consortium and this report was produced from data collected in, and received from, 22 countries in 2008 and 18 countries in 2009. Our thanks go to the authors, national teams, researchers, funding bodies and other contributors who have made this possible.",

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Bayesian network analysis for the dynamic prediction of early stage entrepreneurial activity index

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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