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All Journal Applied Information System and Management
Liko Noor Rafianto Rahadian
Lambung Mangkurat University
Languange, Linguistic, Communication & Media

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Understanding Online Purchase Intention in Social Commerce: The Roles of Web Quality, User Satisfaction, and Algorithmic Personalization Megi Nediawan; Asrid Juniar; Liko Noor Rafianto Rahadian; Muhammad Rayyan Adhiyani; Nasywa Ghaida Salsabila; Paarth Agarwal
Applied Information System and Management (AISM) Vol. 9 No. 1 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i1.48916

Abstract

This study examines how web quality influences online purchase intention through user satisfaction in social commerce, with a specific focus on TikTok Shop. Drawing on the WebQual 4.0 framework and expectation-confirmation theory, the research investigates the effects of usability quality, information quality, and service interaction quality on user satisfaction and how satisfaction subsequently drives online purchase intention. Extending prior WebQual research, this study integrates algorithmic personalization as a moderating mechanism that strengthens the satisfaction–online purchase intention relationship, addressing an aspect largely overlooked in existing social commerce studies. Data were collected from 210 TikTok Shop users in Banjarmasin and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that all web quality dimensions significantly enhance user satisfaction, which in turn positively affects online purchase intention. Algorithmic personalization significantly reinforces this relationship, and the model explains 44.6% of the variance in online purchase intention (R² = 0.446). The findings contribute to theory by extending WebQual to algorithm-driven social commerce contexts and offer practical insights for platforms seeking to optimize user experience and personalized recommendation strategies.
Co-Authors Asrid Juniar Megi Nediawan Muhammad Rayyan Adhiyani Nasywa Ghaida Salsabila Paarth Agarwal