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

Sohn, So Young; Lee, Ann Sung

doi:10.1016/j.eswa.2013.01.009

Cited by 14 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e network big data is constructed on the user's real network situation, collects objective data by not intervening in user behavior activities, so it has high ecological validity. e sample size is small and the representativeness is limited [17].…”

Section: Traditional Research Techniquesmentioning

confidence: 99%

Analysis of Influencing Factors of Social Mental Health Based on Big Data

Zhao¹,

Tang

2021

Mobile Information Systems

View full text Add to dashboard Cite

Big data is a large-scale rapidly growing database of information. Big data has a huge data size and complexity that cannot be easily stored or processed by conventional data processing tools. Big data research methods have been widely used in many disciplines as research methods based on massively big data analysis have aroused great interest in scientific methodology. In this paper, we proposed a deep computational model to analyze the factors that affect social and mental health. The proposed model utilizes a large number of microblog manual annotation datasets. This huge amount of dataset is divided into six main factors that affect social and mental health, that is, economic market correlation, the political democracy, the management law, the cultural trend, the expansion of the information level, and the fast correlation of the rhythm of life. The proposed model compares the review data of different influencing factors to get the correlation degree between social mental health and these factors.

show abstract

Section: Traditional Research Techniquesmentioning

confidence: 99%

Analysis of Influencing Factors of Social Mental Health Based on Big Data

Zhao¹,

Tang

2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Finally, the fields of AIs and big data develop computational models to handle large amount of information nondeterministically, and they can be applied literally as decision support systems for people running new ventures or innovation projects (cf. Dellermann et al 2018;Sohn and Lee 2013;Babovic 2011, 2012). Future studies can explore how entrepreneurs can use AI nondeterministic models as decision support for pathdependent decision-making under uncertainty.…”

Section: Learning From Ai: Further Researchmentioning

confidence: 99%

Advancing entrepreneurship as a design science: developing additional design principles for effectuation

Zhang

Burg

2019

Small Bus Econ

View full text Add to dashboard Cite

Scholars have advocated the development of entrepreneurship as a design science. One foundational challenge in a design science is to identify design principles. We argue that a particular field can draw on a design knowledge from different design sciences to develop design principles. In particular, we show that entrepreneurship research can learn from one branch of artificial intelligence studies called Bgenetic algorithm,ŵ hich is a design field that creates solutions for complex, nonanalytical, and ill-structured problems. We illustrate the analogous transfer process by identifying complementary design principles for one exemplary entrepreneurship theory, namely effectuation. In turn, these additional effectual design principles further effectuation theory as a design science and help advance entrepreneurship as a nascent design science. Imaginative thinking informed by a variety of (embodied) knowledge to create new and transform existing artifacts.

show abstract

“…Recently, a paper by Sohn and Lee (2013) has been published that proposed 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 is also one of the possibilities for future research on and prediction of early-stage entrepreneurship, as it can be used to predict various aspects of entrepreneurial aspiration of the following year.…”

Section: Discussionmentioning

confidence: 99%

Country’s Development as a Determinant of Early-Stage Entrepreneurial Activity

et al. 2013

View full text Add to dashboard Cite

5national institute of chemistry, hajdrihova 19, 1001 ljubljana, slovenia our study is built on the dependence of early-stage entrepreneurial activity on GdP per capita, GdP real growth rate, unemployment rate, inflation rate, investments and public debt of different countries. We divide the early-stage entrepreneurial activity into necessity-driven and improvement-driven opportunistic entrepreneurial activity. to establish the dependencies we have conducted the regression analyses. our three main findings are: (a) early-stage entrepreneurial activity does depend on our predictors; (b) necessity-driven entrepreneurial activity is negatively correlated to country's development; and (c) improvement-driven opportunistic entrepreneurial activity is positively correlated to country's development.

show abstract

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

Cited by 14 publications

References 28 publications

Analysis of Influencing Factors of Social Mental Health Based on Big Data

Analysis of Influencing Factors of Social Mental Health Based on Big Data

Advancing entrepreneurship as a design science: developing additional design principles for effectuation

Country’s Development as a Determinant of Early-Stage Entrepreneurial Activity

Contact Info

Product

Resources

About