实时电子商务中用户粘性的形成机制:PLS-SEM 和 ANN 混合方法

Lin Wang, Huiyu Zhu, Xia Li, Yang Zhao
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摘要

目的虽然用户粘性在电子商务直播领域已被研究了数年,但在这一领域,很少有人关注流媒体属性对用户粘性的影响。本研究以刺激-机体-反应(S-O-R)理论为基础,探讨了流媒体属性如何影响用户粘性。作者从中国电商直播用户中获取了 496 个有效样本,并使用偏最小二乘法结构方程建模(PLS-SEM)探讨了用户粘性的形成。人工神经网络(ANN)用于捕捉线性和非线性关系,并分析重要变量的归一化重要性排序,对 PLS-SEM 结果进行补充。专业知识和可信度对感知信息质量有积极影响。此外,流媒体品牌偏好在 PSI 与用户粘性之间以及感知信息质量与用户粘性之间起到了中介作用。与 PLS-SEM 相比,ANN 的预测能力更为稳健。此外,PLS-SEM 和 ANN 的结果都表明,吸引力是用户粘性的最强预测因子。原创性/价值本研究解释了流媒体属性如何影响用户粘性,为今后研究直播电商中的用户行为提供了参考价值。基于 ANN 对变量间线性和非线性关系的探索是对现有研究的补充。此外,本研究的结果对从业者如何提高用户粘性也有借鉴意义,有助于直播行业的发展。
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Formation mechanism of user stickiness in live e-commerce: the hybrid PLS-SEM and ANN approach
PurposeAlthough user stickiness has been studied for several years in the field of live e-commerce, little attention has been paid to the effects of streamer attributes on user stickiness in this field. Rooted in the stimulus-organism-response (S-O-R) theory, this study investigated how streamer attributes influence user stickiness.Design/methodology/approachThe authors obtained 496 valid samples from Chinese live e-commerce users and explored the formation of user stickiness using partial least squares-structural equation modeling (PLS-SEM). Artificial neural network (ANN) was used to capture linear and non-linear relationships and analyze the normalized importance ranking of significant variables, supplementing the PLS-SEM results.FindingsThe authors found that attractiveness and similarity positively impacted parasocial interaction (PSI). Expertise and trustworthiness positively impacted perceived information quality. Moreover, streamer-brand preference mediated the relationship between PSI and user stickiness, as well as the relationship between perceived information quality and user stickiness. Compared to PLS-SEM, the predictive ability of ANN was more robust. Further, the results of PLS-SEM and ANN both showed that attractiveness was the strongest predictor of user stickiness.Originality/valueThis study explained how streamer attributes affect user stickiness and provided a reference value for future research on user behavior in live e-commerce. The exploration of the linear and non-linear relationships between variables based on ANN supplements existing research. Moreover, the results of this study have implications for practitioners on how to improve user stickiness and contribute to the development of the livestreaming industry.
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