Analytical Study of User Privacy Behaviour and Dark Pattern Detection in Nigerian E-Commerce Platforms

Dark patterns in digital user interface design manipulate users into unintended actions including sharing personal data beyond their intentions, and their prevalence in Nigerian e-commerce platforms has not been analytically investigated despite the rapid growth of digital commerce. This study analytically examines user privacy behaviour and the presence of dark patterns in Nigeria's leading e-commerce platforms. A two-phase mixed-methods design was applied. Phase one conducted a structured heuristic analysis of six major Nigerian e-commerce platforms using an extended dark pattern taxonomy framework, documenting dark pattern instances by category, severity, and data impact. Phase two administered a survey to 300 regular e-commerce users in Lagos and Abuja on privacy attitude-behaviour consistency, dark pattern awareness, and susceptibility indicators. Multiple regression was applied to survey data. Available dark patterns research from African digital commerce contexts identifies roach motel patterns (difficult account deletion), hidden subscription activation, and forced consent for data collection as the most prevalent dark pattern forms in regional platforms. The Privacy Calculus Theory and the Cognitive Load Theory provide the analytical framework for user susceptibility analysis. Findings from phase one identified dark patterns in all six platforms, with hidden data consent as the highest-frequency category. Phase two findings reveal a significant privacy paradox where stated privacy concern scores do not predict privacy-protective behaviour. Recommendations address FCCPC mandatory dark pattern prohibition guidelines and consumer awareness campaigns. Keywords: dark patterns, user privacy, e-commerce, Nigeria, digital consumer protection.