Algorithmic Influence and Ethical Boundaries in Online Consumer Behavior

Abstract

The rapid advancement of artificial intelligence (AI) has transformed digital commerce by enabling algorithm-driven personalization that increasingly influences online consumer behavior. While algorithmic recommendation systems improve decision-making efficiency and customer engagement, growing concerns regarding transparency, fairness, privacy, accountability, and consumer autonomy have intensified discussions surrounding the ethical implications of AI-driven personalization. Despite extensive research on algorithmic marketing, existing studies remain fragmented, with limited integration of ethical considerations into comprehensive explanations of online consumer behavior. Therefore, this study aims to systematically synthesize the existing literature on algorithmic influence and ethical boundaries and to develop a conceptual framework that explains their relationships with online consumer behavior. This study employed a Systematic Literature Review with Conceptual Framework Development (SLR-CFD) following the PRISMA 2020 guidelines. Relevant peer-reviewed journal articles published between 2020 and 2026 were retrieved from the Scopus and Web of Science databases using a structured search strategy, complemented by backward and forward snowballing techniques. The reviewed studies were analyzed through thematic synthesis to identify dominant research themes and emerging knowledge gaps. The findings reveal four major research themes: algorithmic influence in digital commerce, ethical boundaries of AI-driven personalization, consumer psychological responses, and online consumer behavioral outcomes. Based on these findings, this study proposes an integrated conceptual framework positioning ethical evaluation as the central mechanism linking algorithmic influence to consumer behavior while identifying potential moderating factors, including AI literacy, digital experience, privacy concern, and regulatory environment. This study contributes to the literature by consolidating fragmented research into a coherent theoretical framework and provides practical insights for developing transparent, fair, and human-centered AI systems that foster sustainable digital consumer relationships.