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

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Giacomo Tollo

    (Université du Luxembourg)

  • Joseph Andria

    (University of Palermo)

  • Stoyan Tanev

    (Sprott School of business, Carleton University)

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.

Suggested Citation

  • Giacomo Tollo & Joseph Andria & Stoyan Tanev, 2021. "Neural Networks to Determine the Relationships Between Business Innovation and Gender Aspects," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 193-199, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-78965-7_29
    DOI: 10.1007/978-3-030-78965-7_29
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