JOURNAL ARTICLE

Bollywood's Bad Guys: Deconstructing the Representation of Criminality and Justice in Popular Indian Cinema.

  • Published In: Journal of African Film & Diaspora Studies (JAFDIS), 2026, v. 9, n. 1. P. 167 1 of 3

  • Database: Humanities Source Ultimate 2 of 3

  • Authored By: Okpa, John Thompson; Enamhe, Dorn Cklaimz; Otiwa, James Obriku; Utsubasha, Rose Agba; Udah, Enang Bassey; Ndum, Etim Victor; John-Okpa, Promise Akunna 3 of 3

Abstract

This article examines the evolving representation of criminality and justice in popular Indian cinema, specifically Bollywood, highlighting how cinematic villains have transformed from one-dimensional caricatures into complex, multi-layered characters that reflect broader societal anxieties. Anchored in Albert Bandura’s social learning theory, the study uses qualitative thematic analysis of secondary sources to show that this shift includes the romanticization of anti-heroes who challenge corrupt systems and the normalization of extra-legal vigilantism as a response to perceived inefficiencies in formal justice institutions. Furthermore, the villain archetype has expanded from individual antagonists to entire institutions such as the police and judiciary, symbolizing systemic corruption and public mistrust. The article concludes by recommending that filmmakers adopt more nuanced portrayals of justice and that audiences develop media literacy to critically engage with these narratives, thereby fostering informed public discourse rather than reinforcing mistrust and glorification of vigilante violence.

Additional Information

  • Source:Journal of African Film & Diaspora Studies (JAFDIS). 2026/03, Vol. 9, Issue 1, p167
  • Document Type:Article
  • Subject Area:Film
  • Publication Date:2026
  • ISSN:25162705
  • DOI:10.31920/2516-2713/2026/v9n1a8
  • Accession Number:193029452
  • Copyright Statement:Copyright of Journal of African Film & Diaspora Studies (JAFDIS) 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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