Thinking or Feeling? How AI Recommender Types and Algorithmic Matching Frames Shape Consumers’ Online Green Purchasing Intentions

Promoting consumers’ green consumption is an important way to drive the low-carbon transition on the demand side and achieve sustainable development. Existing research mainly compares the persuasive effects of AI and human recommenders, with limited attention to how different types of AI recommenders and algorithmic matching frames can collaboratively promote online green purchasing. Through two studies, this work examines the impact of AI recommender types (thinking vs. feeling) and algorithmic matching frames (item-oriented vs. user-oriented) on online green purchasing intentions and their psychological mechanisms. The results indicate that there is a significant interaction effect; combining thinking AI with an item-oriented frame is more effective in enhancing purchasing intentions, while combining feeling AI with a user-oriented frame is even more effective. Furthermore, in the thinking AI context, the item-oriented frame enhances purchasing intentions by increasing perceived product efficacy; in the feeling AI context, the user-oriented frame functions by enhancing perceived social identification. This work reveals the interaction between AI recommender types and algorithmic matching frames, as well as their differentiated psychological mechanisms. It expands the application of Schema Congruity Theory in AI-driven green consumption contexts and provides practical insights for digital platforms to develop AI recommendation and information intervention strategies.