From Traffic to Quality: A Study on the Dual-Path Driving Effects of Streamer Traits on Consumer Trust and Identification

This study is based on the practical context of the livestream e-commerce industry’s shift from “traffic competition” to “quality competition”. Addressing the limitations of existing research that predominantly focuses on streamers’ external traits while overlooking intrinsic qualities and frequently employs linear models that oversimplify the decision-making processes of consumer purchasing behavior (CPB), a theoretical framework grounded in the Elaboration Likelihood Model (ELM) is developed to explain how streamer traits drive consumer trust and identification through dual pathways. This study adopted a mixed-method approach combining structural equation modeling (SEM) and artificial neural networks (ANNs). By analyzing 408 valid questionnaires, it systematically investigated the driving mechanisms through which streamer traits affected consumers’ trust and identification. The study found that streamers’ integrity significantly enhanced perceived trust and perceived identification via the central route. While awareness could strengthen identification, it had no significant effect on trust building, revealing the inherent tension between “traffic” and “quality”. ANN analysis further demonstrated that the nonlinear combination of traits more effectively predicts consumer responses than traits. This study provided empirical support for the “quality transformation” of livestream e-commerce from both theoretical and methodological perspectives, offering important implications for platforms to develop a quality assessment system centered on trust and identification and to optimize the streamer cultivation mechanism.