Neural Networks to Determine the Relationships Between Business Innovation and Gender Aspects

Giacomo Tollo (), Joseph Andria () and Stoyan Tanev ()
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Giacomo Tollo: Université du Luxembourg
Joseph Andria: University of Palermo
Stoyan Tanev: Sprott School of business, Carleton University

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2021, pp 193-199 from Springer

Abstract: Abstract Gender aspects of management, innovation and entrepreneurship are gaining more and more importance as cross-cutting issues for researchers, practitioners and decision makers. Extant literature pays a growing attention to the hypothesis that there exists a correlation between the gender diversity of corporate boards of directors and the business attitude to innovation. In this paper we introduce a working framework to test the aforementioned hypothesis and to examine the correlation between board diversity and innovation perception of a business. This framework is based on correlation computation and feed-forward neural networks, and it is used to evaluate whether the gender component may be used to predict the innovation perception of a business. First results about three different economic scenarios are reported and discussed.

Keywords: Innovation and entrepreneurship; Gender diversity; Corporate boards of directors; Perception of innovation; Feed-forward neural networks (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-78965-7_29

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DOI: 10.1007/978-3-030-78965-7_29

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