Towards a cybercontextual transmission model for online scamming

Abstract

This study focuses on advance fee fraud (AFF) scamming, a specific form of online deception in which scammers rely on social engineering techniques to deceive individuals into making advance payments to them. Several industry and law enforcement reports have emphasised that AFF scamming is among the most pervasive forms of online social engineering attacks against consumers, organisations, and online users. Although AFF scamming has received significant attention worldwide, it remains an under-researched and poorly understood crime, and little work has focused on offenders. Although studies on online scammers have inferred that digital environment attributes influence online deception, few studies have empirically clarified how such contexts explain online scammers’ motivations. The present study was designed to explore the motivations and deceptive practices of modern-day AFF scammers by using data from scammers. The empirical results urge the adoption of a model for AFF scamming that conceptually builds on social learning theory (SLT)’s core concepts but functions differently from it, warranting a new IT-based conceptual model. Accordingly, our contributions identify and explain cybercontextual social learning attributes that influence AFF scamming and underscore how traditional criminological theories, such as SLT, cannot sufficiently account for online offences, such as AFF scamming. Consequently, we propose cybercontextual transmission model (CTM) as a reformulation of SLT. Additional theory and practice implications are discussed.