Applying the S-O-R Model to Algorithmic Commerce: How TikTok’s Recommendation System Stimulates Impulsive Consumer Behavior
Bibliographic record
Abstract
Short-video platforms have rapidly evolved from entertainment spaces into major drivers of digital commerce, and TikTok represents a leading case due to its seamless integration of algorithmic curation with embedded shopping and livestreaming features. This paper examines how TikTok’s recommendation algorithms stimulate impulsive consumer behavior through the lens of the S-O-R framework. By conceptualizing personalized recommendations, social proof signals, and scarcity cues as stimuli, this study investigates how these platform-specific triggers activate psychological mechanisms such as emotional arousal, flow, trust, and fear of missing out (FOMO). These organisms, in turn, are shown to facilitate immediate and unplanned purchasing responses. The analysis highlights that TikTok’s architecture functions not as a neutral distribution system but as a behavioral environment designed to compress decision-making and amplify consumer engagement. The study contributes theoretically by extending the application of the S-O-R model to algorithmic and dynamic social commerce contexts, and practically by offering insights into how brands and platforms may strategically leverage, yet responsibly manage, algorithmic influence. It also raises ethical considerations about autonomy, overconsumption, and protection of vulnerable users. Overall, this research demonstrates that TikTok exemplifies the power and risks of algorithmic marketing in shaping consumer behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".