The Influence of Artificial Intelligence-Generated Content on Consumers' Purchase Decision in E-commerce: An Action Mechanism Study

Abstract

Consumers making choices in e-commerce are now organized by artificial intelligence-generated and -orchestrated content (AIGC). This study looks at two typical surfaces, namely, algorithmic recommendations and urgency cues, through an exploratory secondary analysis of a survey (N=102) of young and digitally active shoppers. This paper has constructed concise variables on the basis of the questionnaire, and reports descriptive statistics, chi-square screens as well as two parsimonious logistic regressions. Results indicate that reported influence by recommendations is positively associated with comfort about data collection and shopping frequency, while chatbot exposure shows a negative association; dynamic-pricing exposure shows a borderline positive signal. Reported influence by urgency messages is predicted by perceived preference accuracy and trust in AI over human service. AI familiarity, perceived intrusiveness, perceived bias, overall satisfaction, and basic demographics do not reach conventional significance in either model; chi-square tests similarly show no detectable segmentation by gender or education in the tested outcomes. Together, the findings suggest that influence depends less on generic awareness and more on situated perceptions (accuracy, trust, data comfort) and context of use (task readiness). This paper translates these patterns into actionable guidance on transparency, explainability, pricing fairness cues, chatbot orchestration, and responsible urgency, while noting limitations of sample scope, self-reporting, and model convergence.

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