UGC or Chatbot? Two Strategies, One Goal: How UGC and Chatbots Divergently Reduce Online Product Uncertainty

Abstract

This study scrutinizes two predominant uncertainty reduction strategies in online retail: User-Generated Content (UGC) which stems from active strategy and chatbots which functions as an interactive strategy. Drawing on Uncertainty Reduction Theory (URT) and analyzing data from 455 online shoppers using Covariance-Based Structural Equation Modeling (CB-SEM), we examine how these strategies reduce product uncertainty across three dimensions (description, performance, fit) and influence purchase intention. Results reveal that UGC characteristics (trustworthiness, valence, information richness) significantly reduce all uncertainty types, with information richness being particularly impactful for performance (β=0.38) and description (β=0.42) uncertainty. Chatbot dimensions show more specialized effects: while anthropomorphism and social presence reduce fit uncertainty (β=0.27, β=0.28), media richness shows no significant effect on performance or description uncertainty. The findings demonstrate UGC's comprehensive uncertainty reduction capabilities versus chatbots' specialized effectiveness for fit-related uncertainty, providing theoretical and practical insights for optimizing online retail strategies.