JOURNAL ARTICLE

African Folktales as Indigenous Intelligence: A Metaphor for Demystifying Global Investment Fraud.

  • Published In: Journal of African Languages & Literary Studies (JoALLS), 2026, v. 7, n. 1. P. 157 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Agunbiade, Oyewumi Olatoye; Enongene, Sone Mirabeau; Olaiya, Kolawole 3 of 3

Abstract

The article examines African folktales as a form of indigenous intelligence capable of demystifying global investment fraud, particularly Ponzi schemes prevalent in South Africa and across the African continent. Using a new historicist and metaphorical analysis of Femi Osofisan’s play *Love’s Unlike Lading: A Comedy from Shakespeare*—an adaptation of Shakespeare’s *The Merchant of Venice*—the study highlights how indigenous knowledge embedded in folklore, proverbs, and oral literature provides cognitive tools to recognize and resist financial scams. It argues that modern financial literacy alone is insufficient to protect investors, advocating for the integration of African indigenous knowledge and folktales into educational curricula, especially in business schools, to equip individuals with the intelligence needed to navigate and avoid fraudulent investment schemes. The research underscores the cultural and pedagogical value of African oral traditions as a moral guide and practical resource against contemporary economic intrigues.

Additional Information

  • Source:Journal of African Languages & Literary Studies (JoALLS). 2026/03, Vol. 7, Issue 1, p157
  • Document Type:Literary Criticism
  • Subject Area:Literature and Writing
  • Publication Date:2026
  • ISSN:2633-2108
  • DOI:10.31920/2633-2116/2026/v7n1a8
  • Accession Number:192859188
  • Copyright Statement:Copyright of Journal of African Languages & Literary Studies (JoALLS) is the property of Adonis & Abbey Publishers Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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