@ARTICLE{Knap-Stefaniuk2026:i9048,
	author="{Knap-Stefaniuk, Agnieszka and Maciaszczyk, Magdalena and Kocot, Damian and Pietrzykowska, Marta and Gontarek, Izabela and Montano, Aneta and Bednarczyk, Monika}",
	title="{Analysis of Dark AI Patterns Manipulation Vulnerability Levels in the E-Commerce Environment}",
	year="2026",
	type="artykuł naukowy",
	language="en",
	journal="{European Research Studies Journal}",
	issn="1108-2976",
	volume="29",
	number="1",
	pages="378--390",
	doi="10.35808/ersj/4317",
	url="https://ersj.eu/journal/4317",
	keywords="Dark AI; e-commerce; manipulation vulnerabilities; artificial intelligence; trade",
	abstract="{Purpose: The study aims to examine the levels of consumer susceptibility to Dark AI Patterns in the e-commerce environment and to identify the psychological and behavioral factors that shape this vulnerability. Design/Methodology/Approach: The research was conducted using an online survey carried out in 2025 on a sample of 429 respondents, measuring their responses to algorithmic pressure techniques such as scarcity cues, countdown timers, social proof, and AI-driven recommendations. Findings: The results indicate a moderate and relatively uniform susceptibility to Dark AI Patterns, with higher personalization and trust in AI increasing vulnerability, while greater algorithmic awareness plays a protective role. Practical Implications: The study highlights the need for responsible design of AI-enhanced interfaces, emphasizing transparency, limitation of manipulative cues, and support for users’ informed decision-making. Originality/Value: The article provides one of the first empirical assessments of consumer susceptibility to AI-driven manipulative design, offering insights relevant for researchers, practitioners, and regulators shaping the future of ethical e-commerce.}",
}