Supplementary Files
Keywords
Data Analytics
Entrepreneurship
Predictive Modelling
Systematic Literature Review
How to Cite
Abstract
Entrepreneurship drives economic and social progress, yet predicting key attributes such as cognitive (e.g., creativity, opportunity recognition), emotional (e.g., resilience, emotional intelligence), and social (e.g., leadership, communication) traits remains challenging due to the limitations of traditional methods. Traditional methods lack precision, driving the adoption of artificial intelligence (AI)-based data analytics tools to analyse complex datasets. This paper employs a Systematic Literature Review (SLR) to synthesise 50 peer-reviewed studies from 2018-2024, using thematic analysis to categorise AI-based data analytics tools into supervised learning, unsupervised learning, natural language processing (NLP), and deep learning, mapping their applications to entrepreneurship attributes. Findings highlight the dominance of models like Random Forests and Transformers in predicting entrepreneurship attributes, with gaps in exploring non-financial attributes such as creativity and resilience, and ethical considerations, including data bias and privacy concerns. This study investigates opportunities and challenges for Malaysian Higher Education Institutions (HEIs), emphasising personalised education and research advancements against barriers such as bias and resource constraints. Contributions include a novel taxonomy that maps AI-based data analytics tools to entrepreneurship attributes and incorporates an ethical evaluation dimension, alongside practical insights for stakeholders and a research agenda for ethical, inclusive AI adoption in entrepreneurship globally and in Malaysia.
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