The Impact of In-Feed Recommendation on Consumers' Impulse Buying Behavior on Short Video Platforms

The widespread adoption of algorithm-driven short video platforms has transformed how users process content and engage in impulsive purchasing. However, current models often fail to capture the multi-dimensional psychological mechanisms that underlie such behavior, particularly the dynamic interplay between content signals, emotional immersion, perceptual intuition, and algorithmic perception. To address these limitations, this study proposes stimulus-organism-response–elaboration likelihood model, an integrated behavioral model that fuses the stimulus–organism–response paradigm with dual-route elaboration (central and peripheral), flow experience, and algorithmic trust. The model differentiates between cognitive and behavioral impulsivity, incorporates post-purchase regret as a feedback mechanism, and models personalization and transparency as precursors to algorithmic attitude and trust.