Shopping goal specificity matters: How algorithmic recommendations influence adoption intention

In the age of online shopping, consumers frequently engage with shopping apps in hybrid modes, switching between goal-specific searches and serendipitous browsing. This behavior poses a pressing challenge for algorithm-driven recommendation systems. While existing studies prioritize algorithmic accuracy, they largely neglect the dynamic congruence between recommendations (content-based vs. user-based) and consumers' shifting goal specificity. This study addresses the aforementioned gap by focusing on adaptive algorithmic recommendation strategies. Drawing on the information processing framework of the information adoption model with the decision-making mechanism in shopping goals theory, we examine the impact of the two predominant algorithmic recommendations (content-based and user-based) on recommendation adoption intention. Our hypotheses are verified by the results of three 2 × 2 between-subjects experiments. The findings reveal that content-based (user-based) recommendations are more effective when consumers have high (low) specific goals, and this effectiveness is due to the fact that matching the two enhances consumers’ perceived diagnosticity (serendipity). Furthermore, the interaction between shopping goal specificity and algorithmic recommendations is moderated by shopping motivation. Building on shopping goals theory and information adoption model, these findings advance recommendation systems literature by establishing goal specificity as a critical moderator and introducing dual cognitive pathways in information adoption. This insight not only resolves the efficiency of recommendation systems but also provides actionable insights for segment-specific targeting in online shopping.