“Dark patterns” - To what extent are user attitudes and behaviors influenced by incentive and deceptive mechanisms in the user interface (UI)? A comparative analysis based on a taxonomy

Details

Supervisors

de Moerloose, Chantal

Faculty

Louvain School of Management

Degree label

Master [120] en sciences de gestion, à finalité spécialisée

Abstract

This study investigates the influence of dark patterns—manipulative design tactics in digital interfaces—on user attitudes and behaviors. The primary objective is to develop a comprehensive classification system for dark patterns and empirically validate their impact on users’ self-reported decision-making, emotions, trust, and loyalty. By using a structured survey distributed to 159 participants, and by employing t-tests and ANOVA analysis, the research analyzes various examples of dark patterns to assess their effects. Findings reveal that dark patterns do not significantly impact overall decision-making, contradicting existing literature. However, they elicit strong negative emotional responses, undermine trust, and reduce user loyalty. The study also confirms that user awareness significantly mitigates the impact of dark patterns, though age and IT confidence do not play significant roles. The proposed classification in this study differentiates dark patterns based on their degree of obligation, deception, and visibility, providing a refined framework for understanding these manipulative tactics. The study reveals that forced dark patterns are found to be more blameworthy compared to oriented ones, deceptive dark patterns are more blameworthy than manipulative ones, and hidden dark patterns are more blameworthy than visible ones. Theoretical implications include advancing the understanding of dark patterns by validating their influence on user behavior. From a managerial perspective, the findings emphasize the ethical responsibility of designers and managers to avoid manipulative tactics that can harm user relationships and brand reputation. Practical recommendations include avoiding manipulative design practices, implementing ethical design training, incorporating user feedback mechanisms, and conducting regular audits. Despite some limitations, such as potential survey biases and a limited number of dark pattern examples, this research offers valuable insights and suggests future studies should explore broader variables, longitudinal effects, AI detection methods, trust recovery strategies, and the mental health impacts of dark patterns.