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Effectiveness of recommendation algorithms on impulsive buying in e-commerce platforms: A systematic literature review

Author

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  • Nuriya Fadilah

    (Universitas Trunojoyo Madura)

  • Itaul Masarroh

    (Universitas Trunojoyo Madura)

  • Muhammad Alkirom Wildan

    (Universitas Trunojoyo Madura)

Abstract

This study analyzes the effectiveness of recommendation algorithms in influencing impulsive buying behavior on e-commerce platforms. Through a comprehensive review of the existing research literature, it was revealed that personalization strategies such as collaborative filtering, content-based filtering, and artificial intelligence (AI) boost impulsive buying tendencies by alleviating cognitive burdens and enhancing elements such as limited-time offers, social proof, and emotional connection. Factors such as flow experience, positive feelings, and moderating elements such as age, social media influence, and economic circumstances also play a crucial role in determining the effectiveness of these algorithms. This study provides beneficial knowledge for algorithm developers and digital marketers to refine personalization efforts and to consider psychological and contextual influences when crafting more impactful marketing strategies.

Suggested Citation

  • Nuriya Fadilah & Itaul Masarroh & Muhammad Alkirom Wildan, 2025. "Effectiveness of recommendation algorithms on impulsive buying in e-commerce platforms: A systematic literature review," Priviet Social Sciences Journal, Privietlab Research Center, vol. 5(8), pages 186-198, August.
    • Handle: RePEc:prv:pssjpv:485
    DOI: 10.55942/pssj.v5i8.485
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