Artificial Intelligence in the Service of Entrepreneurial Finance:
Knowledge Structure and the Foundational Algorithmic Paradigm
- URL: http://arxiv.org/abs/2311.13213v1
- Date: Wed, 22 Nov 2023 07:58:46 GMT
- Title: Artificial Intelligence in the Service of Entrepreneurial Finance:
Knowledge Structure and the Foundational Algorithmic Paradigm
- Authors: Robert Kudeli\'c and Tamara \v{S}maguc and Sherry Robinson
- Abstract summary: The study provides a bibliometric review of Artificial Intelligence applications in entrepreneurial finance literature.
The bibliometric analysis gives a rich insight into the knowledge field's conceptual, intellectual, and social structure.
- Score: 0.8287206589886879
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: While the application of Artificial Intelligence in Finance has a long
tradition, its potential in Entrepreneurship has been intensively explored only
recently. In this context, Entrepreneurial Finance is a particularly fertile
ground for future Artificial Intelligence proliferation. To support the latter,
the study provides a bibliometric review of Artificial Intelligence
applications in (1) entrepreneurial finance literature, and (2) corporate
finance literature with implications for Entrepreneurship. Rigorous search and
screening procedures of the scientific database Web of Science Core Collection
resulted in the identification of 1890 relevant journal articles subjected to
analysis. The bibliometric analysis gives a rich insight into the knowledge
field's conceptual, intellectual, and social structure, indicating nascent and
underdeveloped research directions. As far as we were able to identify, this is
the first study to map and bibliometrically analyze the academic field
concerning the relationship between Artificial Intelligence, Entrepreneurship,
and Finance, and the first review that deals with Artificial Intelligence
methods in Entrepreneurship. According to the results, Artificial Neural
Network, Deep Neural Network and Support Vector Machine are highly represented
in almost all identified topic niches. At the same time, applying Topic
Modeling, Fuzzy Neural Network and Growing Hierarchical Self-organizing Map is
quite rare. As an element of the research, and before final remarks, the
article deals as well with a discussion of certain gaps in the relationship
between Computer Science and Economics. These gaps do represent problems in the
application of Artificial Intelligence in Economic Science. As a way to at
least in part remedy this situation, the foundational paradigm and the bespoke
demonstration of the Monte Carlo randomized algorithm are presented.
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