Original scientific paper

https://doi.org/10.17559/TV-20250906002961

Revisiting the S-O-R Framework in Live-Streaming Commerce: An AI-Driven Emotion Recognition Approach

Lei Mei ; School of Business, Geely University of China, Chengdu, 641423, China; Farabi Business School, Al-Farabi Kazakh National University, Kazakhstan
Yang Yang ; School of Business, Geely University of China, Chengdu, 641423, China *
Yuqin Deng ; School of Business, Geely University of China, Chengdu, 641423, China

* Corresponding author.

Abstract

Live-streaming commerce has fundamentally reshaped online purchasing behavior by merging real-time interaction with digital consumption. This study advances the classical Stimulus-Organism-Response (S-O-R) framework by embedding artificial intelligence-driven emotion recognition to quantify consumersꞌ affective and social responses. Drawing on data from 1443 Douyin users engaged with Hongqi Chainꞌs livestream sessions, we employed logistic regression and robustness validation in R to examine how streamer interactivity, professionalism, and attractiveness affect emotional response and social presence - two key organism constructs - there by influencing purchase intention. The inclusion of AI-derived emotion metrics, obtained from a fine-tuned Chinese BERT sentiment model, improved the modelꞌs explanatory power by 5.6% and raised the adjusted R² to 0.861. Social presence emerged as the dominant mediator linking streamer stimuli to behavioral intention. The findings reconceptualize the organism stage of the S-O-R model within an AI-enhanced analytical paradigm and provide managerial implications for optimizing live-streaming strategies through data-driven consumer insight.

Keywords

AI-driven emotion recognition; consumer purchase intention; live-streaming commerce; sentiment analytics; short-video platforms; social presence; stimulus-organism-response (S-O-R) framework

Hrčak ID:

348692

URI

https://hrcak.srce.hr/348692

Publication date:

30.6.2026.

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