Shiyu Ma

Ms. Shiyu Ma


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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6 August 2026 in Systems
Verified
Journal Article
Open access
PDF file
LHLong Huang
Long Huang
SMShiyu Ma
Shiyu Ma
YSYanshuo Song
Yanshuo Song
XQXiaodong Qiu
Xiaodong Qiu
AI-powered virtual humans are increasingly used as live streaming presenters, yet it remains unclear how consumers translate experiences with these embodied agents into trust and repurchase intention. Rather than tracing a complete multi-stage consumer journey, this study focuses on a recent virtual human live streaming session as a focal touchpoint that compresses product discovery, evaluation, interaction, and purchase support. Integrating consumer touchpoint logic with the Stimulus–Organism–Response (S-O-R) framework, we examine how perceptual touchpoint experience (active control, synchronicity, and two-way communication) and cognitive touchpoint experience (perceived expertise and perceived competence) shape consumer trust and repurchase intention. Using survey data from 305 consumers with virtual human live streaming experience, this study tested the proposed model via partial least squares structural equation modeling (PLS-SEM) and supplemented the findings with fuzzy-set qualitative comparative analysis (fsQCA). The results showed that both perceptual and cognitive touchpoint experiences significantly enhanced trust, which partially mediated their effects on repurchase intention; cognitive touchpoint experience also exerted a strong direct effect on repurchase intention. The fsQCA results revealed two equifinal configurations leading to high repurchase intention: a full-experience path (high perceptual experience + high cognitive experience + high trust) and a capability-dominant path (low perceptual experience + high cognitive experience + high trust). This study contributes to virtual human commerce research by distinguishing interaction-process cues from competence-based source cues and by showing when AI-powered live streaming can support post-touchpoint retention.