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How cybercriminal communities grow and change: An investigation of ad-fraud communities

Authors :
Jean-Loup Richet
Groupe de Recherche en Gestion des Organisations (GREGOR)
Université Paris 1 Panthéon-Sorbonne (UP1)-IAE Paris - Sorbonne Business School
Source :
Technological Forecasting and Social Change, Technological Forecasting and Social Change, Elsevier, 2022, 174, ⟨10.1016/j.techfore.2021.121282⟩
Publication Year :
2022
Publisher :
HAL CCSD, 2022.

Abstract

Firms spend enormous resources on digital advertising and promoting their brand online. In the meantime, ad-fraud undertaken by cybercriminals cost $42 billion in 2019 and could reach $100 billion by 2023. However, while digital advertisers continue to wrestle with how to effectively counteract ad-fraud, the topic of advertising fraud itself has received little academic attention. Here, we investigate this gap between practice and research through an exploration of ad-fraud communities. Our research implemented a multimethod approach for data collection in a longitudinal (18 months, October 2017 to April 2019) online investigation of this phenomenon. Integrating qualitative and quantitative analysis, we examined (1) internal interactions within ad-fraud communities and (2) ad-fraud communities’ performance and growth. Our online investigation extends our conceptual understanding of ad-fraud and explains how ad-fraud communities innovate. Our findings indicate that capabilities enacted by some communities foster requisite variety and enable the coordination of complex, iterative, and incremental dynamics (cocreation of artificial intelligence-based bots, customer involvement, and reinforcing capabilities). This research has both theoretical and practical implications for innovation in cybercriminal communities. Furthermore, we provide practical guidance for policy-makers and advertisers regarding how to improve their response to business threats. Indeed, a better understanding of how ad-fraud communities innovate enables organizations to develop countermeasures and intelligence capabilities.

Details

Language :
English
ISSN :
00401625
Database :
OpenAIRE
Journal :
Technological Forecasting and Social Change, Technological Forecasting and Social Change, Elsevier, 2022, 174, ⟨10.1016/j.techfore.2021.121282⟩
Accession number :
edsair.doi.dedup.....2cac2808241698bd62ee4e312629a6d6
Full Text :
https://doi.org/10.1016/j.techfore.2021.121282⟩