Applying the S-O-R Model to Algorithmic Commerce: How TikTok’s Recommendation System Stimulates Impulsive Consumer Behavior

Jiashan Li

doi:10.54097/gm717639

Authors

Jiashan Li Department of Sociology, University of Toronto, Toronto, Canada

DOI:

https://doi.org/10.54097/gm717639

Keywords:

Department of Sociology, University of Toronto, Toronto, Canada

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.

Downloads

Download data is not yet available.

References

[1] Shen X, Wang J. How short video marketing influences purchase intention in social commerce: The role of users’ persona perception, shared values, and individual-level factors. Humanities and Social Sciences Communications, 2024, 11: 290. doi:10.1057/s41599-024-02808-w.

[2] Bojic L. AI alignment: Assessing the global impact of recommended systems. Futures, 2024, 160: 103383. doi:10.1016/j.futures.2024.103383.

[3] Yu S, Zhang H, Zheng Q, Chu D D, Chen T, Chen X. Consumer behavior based on the S-O-R model: How do short-video advertisements affect purchase intentions? BioResources, 2024, 19(2): 2639-2659.

[4] Xin Yun, Chun M H. The impact of personalized recommendation on purchase intention under the background of big data. Big Data and Information Analytics, 2024, 8: 80-108. doi:10.3934/bdia.2024005.

[5] Ngo T T A, Nguyen H L T, Mai H T A, Nguyen H P, Mai T H T, Hoang P L. Analyzing the role of customers’ experiences and emotional responses in shaping Generation Z’s impulse buying behavior on Shopee video platform. PLoS ONE, 2025, 20(5): e0322866. doi:10.1371/journal.pone.0322866.

[6] Nguyen D N, Nguyen D V. FOMO and the impulsive purchasing behavior of young people. European Journal of Business and Management Research, 2025, 10(3): 2638. doi:10.24018/ejbmr.2025.10.3.2638.

[7] Obadă D-R, Țugulea O. What drives online impulse buying among Zoomers on TikTok in an emerging market? A new model based on flow theory. Frontiers in Communication, 2024, 9: 1470327. doi:10.3389/fcomm.2024.1470327.

[8] Laroche M, Li R, Richard M-O, Zhou M. An investigation into online atmospherics: The effect of animated images on emotions, cognition, and purchase intentions. Journal of Retailing and Consumer Services, 2022, 64: 102845. doi:10.1016/j.jretconser.2021.102845.

[9] Huang T. Using SOR framework to explore the driving factors of older adults smartphone use behavior. Humanities and Social Sciences Communications, 2023, 10: 690. doi:10.1057/s41599-023-02221-9.

[10] Blagoeva K T, Mijoska M, Pulevska-Ivanovska L. The impact of website design on consumer decision making – evidence from North Macedonia. WSB Journal of Business and Finance, 2023, 57(1): 78-87. doi:10.2478/wsbjbf-2023-0009.

[11] Kexin Z, Teo P-C. The adoption of stimulus-organism-response (SOR) model in the social commerce literature. International Journal of Academic Research in Business and Social Sciences, 2023, 13(7). doi:10.6007/ijarbss/v13-i7/17290.

[12] Wen J, Li X, Han H. Emotional resonance and buying behavior in live streaming: A study on KOL influence and the mediation of purchase intentions. Journal of Theoretical and Applied Electronic Commerce Research, 2025, 20(2): 108. doi:10.3390/jtaer20020108.

[13] Zhang Y, Zhang T, Yan X. Understanding impulse buying in short video live e-commerce: The perspective of consumer vulnerability and product type. Journal of Retailing and Consumer Services, 2024, 79: 103853. doi:10.1016/j.jretconser.2024.103853.

[14] Huang W, Wang X, Zhang Q, Han J, Zhang R. Beyond likes and comments: How social proof influences consumer impulse buying on short-form video platforms. Journal of Retailing and Consumer Services, 2025, 84: 104199. doi:10.1016/j.jretconser.2024.104199.

[15] Xia Y X, Chae S W, Xiang Y C. How do social and media cues induce live streaming impulse buying? SOR model perspective. Frontiers in Psychology, 2024, 15: 1379992. doi:10.3389/fpsyg.2024.1379992.

[16] Feng Z, Al Mamun A, Masukujjaman M, Wu M, Yang Q. Impulse buying behavior during livestreaming: Moderating effects of scarcity persuasion and price perception. Heliyon, 2024, 10(7): e28347. doi:10.1016/j.heliyon.2024.e28347.

[17] Xu H, Gong X, Yan R. Online impulsive buying in social commerce: Mixed-methods research. Information & Management, 2024, 61(3): 103943. doi:10.1016/j.im.2024.103943.

[18] Huo C, Wang X, Sadiq M W, Pang M. Exploring factors affecting consumer’s impulse buying behavior in live-streaming shopping: Interactive research based upon SOR model. SAGE Open, 2023, 13(2). doi:10.1177/21582440231172678.

[19] Gao P, Zeng Y, Cheng Y. The formation mechanism of impulse buying in short video scenario: Perspectives from presence and customer inspiration. Frontiers in Psychology, 2022, 13: 870635. doi:10.3389/fpsyg.2022.870635.

[20] Gallin S, Portes A. Online shopping: How can algorithm performance expectancy enhance impulse buying? Journal of Retailing and Consumer Services, 2024, 81: 103988. doi:10.1016/j.jretconser.2024.103988.