Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang, Lingling; Fu, Yinjun; Yan, Wei; Chen, Huiling; Chun-yu, Xia; Cai, Zhennao

doi:10.3390/app12146907

Cited by 3 publications

(1 citation statement)

References 132 publications

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“…On the other hand, Zhang et al [61] propose an intelligent and reliable prediction model of entrepreneurial intentions to support and guide the positive entrepreneurial intentions of university students. The model mainly uses the improved crowd search algorithm (CSA) to optimise the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to investigate entrepreneurial intention.…”

Section: Review Of the Literaturementioning

confidence: 99%

Characterisation of Youth Entrepreneurship in Medellín-Colombia Using Machine Learning

Ojeda-Beltrán,

Solano-Barliza,

Arrubla-Hoyos

et al. 2023

Sustainability

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The aim of this paper is to identify profiles of young Colombian entrepreneurs based on data from the “Youth Entrepreneurship” survey developed by the Colombian Youth Secretariat. Our research results show five profiles of entrepreneurs, mainly differentiated by age and entrepreneurial motives, as well as the identification of relevant skills, capacities, and capabilities for entrepreneurship, such as creativity, learning, and leadership. The sample consists of 633 young people aged between 14 and 28 years in Medellín. The data treatment was approached through cluster analysis using the K-means algorithm to obtain information about the underlying nature and structure of the data. These data analysis techniques provide valuable information that can help to better understand the behaviour of Colombian entrepreneurs. They also reveal hidden information in the data. Therefore, one of the advantages of using statistical and artificial intelligence techniques in this type of study is to extract valuable information that might otherwise go unnoticed. The clusters generated show correlations with profiles that can support the design of policies in Colombia to promote an entrepreneurial ecosystem and the creation and development of new businesses through business regulation.

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Section: Review Of the Literaturementioning

confidence: 99%

Characterisation of Youth Entrepreneurship in Medellín-Colombia Using Machine Learning

Ojeda-Beltrán,

Solano-Barliza,

Arrubla-Hoyos

et al. 2023

Sustainability

View full text Add to dashboard Cite

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Predicting entrepreneurial intention in Colombian academics: a machine learning approach

Aristizábal,

Tarapuez,

Astudillo

2024

JEEE

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Purpose This study aims to analyze the entrepreneurial intention (EI) of Colombian researchers using machine learning (ML) techniques, considering their academic activity, contexts and social norms (SN). Design/methodology/approach Unsupervised classification techniques were applied, including principal component analysis, hierarchical clustering with the Ward method and a logistic model to evaluate the classification. This was done to group researchers according to their characteristics and EI. Findings The methodology used allowed the identification of three groups of academics with distinct characteristics, of which two showed a high presence of EI. The results indicate that EI is influenced by the connection with the private sector (consulting, intellectual property and applied research) and by the lack of institutional support from universities. Regarding SN, only the preference for entrepreneurial activity over being an employee and the social appreciation of entrepreneurial dedication were identified as predictors of EI. Originality/value The use of ML techniques to study the EI of researchers is uncommon. This study highlights the ability of the methodology used to identify differences between two groups of academics with similar characteristics but different levels of EI. One group was identified that, despite rejecting values associated with entrepreneurs, has a high predisposition to develop a career as an entrepreneur. This provides valuable information for designing policies that promote EI among Colombian researchers.

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How to support innovative small firms? Bibliometric analysis and visualization of start-up incubation

Máté,

Estiyanti,

Novotny

2024

J Innov Entrep

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Supporting the birth and development of innovative small firms, i.e., start-up incubation, has emerged as a critical factor in fostering entrepreneurship, innovation, regional development, and more recently, sustainability. This bibliometric review aims to comprehensively understand and visualize different research perspectives on how start-ups are successfully incubated. A total of 1116 Scopus articles were selected and synthesized using a qualitative approach. The study applies novel visualization techniques (Citation Network Analysis, Global Citation Score, Burst Detection Analysis and Co-Occurrence Networks of Keywords) to map the scientific structure of start-up incubation research, including the clusters of the leading research topics based on citation networks, the most cited articles, and the keywords with the most substantial citation bursts as well as their co-occurrence. We evaluate research on start-up incubation from 1972 to 2023 and show how the topic has advanced by scholars’ changing interests over time. We identify five clusters that reflect distinct study themes and their most cited references. The findings contribute to a more comprehensive understanding of the evolution of start-up incubation research, which developed from a focus on biotechnology and technology transfer toward the current research trends on accelerators and (entrepreneurial) ecosystems. This paper also provides insights for policymakers and start-up incubation managers to make informed decisions.

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Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Cited by 3 publications

References 132 publications

Characterisation of Youth Entrepreneurship in Medellín-Colombia Using Machine Learning

Characterisation of Youth Entrepreneurship in Medellín-Colombia Using Machine Learning

Predicting entrepreneurial intention in Colombian academics: a machine learning approach

How to support innovative small firms? Bibliometric analysis and visualization of start-up incubation

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