Perceived Accuracy and Relevance of AI-Based Product Recommendations and Generation X Purchase Decisions in Jakarta

This study examines how perceived accuracy and contextual relevance of artificial-intelligence-based product recommendations influence purchase decisions among Generation X e-commerce users in Jakarta. A quantitative explanatory design was modeled with 300 respondent profiles and 18 reflective indicators. Partial least squares structural equation modeling, 10,000-resample bootstrapping, coefficient-difference testing, and tenfold cross-validation were applied. Perceived accuracy had a stronger positive effect on purchase decisions (β=.604, p<.001) than relevance (β=.217, p<.001), and the difference was significant (Δβ=.387, p<.001). The model explained 54.2% of purchase-decision variance and showed positive Qpredict2​ values. The findings indicate an accuracy–relevance asymmetry: relevance attracts attention, whereas perceived accuracy more strongly reinforces purchase decisions.