Interest-Based E-Commerce and Users’ Purchase Intention on Social Network Platforms

Abstract

In the modern era of information overload, e-commerce platforms use recommendation systems to predict purchase behavior based on user interests and past actions. Initially limited to e-commerce websites, these systems now leverage the comprehensive data collection capabilities of social network platforms, giving rise to interest-based e-commerce. This study explores factors influencing purchase intentions within this context, using Instagram as an example and applying the Stimulus-Organism-Response (SOR) theory. Data from 313 Generation Z users reveal that interactions with other users and celebrities, along with visually appealing content, significantly enhance perceived enjoyment, which in turn increases purchase intentions. However, self-indulgence did not moderate this relationship. The study extends the SOR theory and provides practical insights for optimizing social media content and engagement strategies to boost purchase intentions, with discussions on implications, limitations, and future research directions.

Keywords

• Interest-based e-commerce
• perceived enjoyment
• purchase intention
• recommendation system
• self-indulgence
• social network platform