Title:
Personalized Recommendation Systems and Consumer Purchase Decision-making: An Exploratory Case Analysis of Data-driven Marketing

Authors:
Longjiang Lin, USA

Abstract:

This study examines how data-driven marketing shapes consumer purchase decision-making through personalized recommendation systems. Using an exploratory qualitative case study design, the paper synthesizes prior literature and analyzes Amazon’s recommendation system as a representative case of data-driven personalization in digital commerce. Rather than estimating causal or statistical effects, the study develops a mechanism-based explanation of how personalized recommendation systems influence consumer behavior. The analysis identifies four main mechanisms: improving information relevance, reducing search costs, increasing product visibility, and shaping alternative evaluation and purchase timing through reminders, bundles, and related-product suggestions. The study also identifies privacy concern, consumer trust, algorithmic transparency, and perceived fairness as boundary conditions that affect the effectiveness of personalization. When consumers perceive data collection as hidden, excessive, or intrusive, personalized marketing may reduce trust and engagement. The study contributes to the literature by linking recommendation systems to specific stages of consumer purchase decision-making and by highlighting the tension between algorithmic personalization and responsible data use. The findings suggest that firms should balance personalization accuracy with transparency, privacy protection, consumer control, and fairness-oriented governance.

DOI: https://doi.org/10.51505/IJEBMR.2026.10716