Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Technology Informatics and Engineering International Journal of Graphic Design
Xiaochen Li
Marketing Analytics, Bentley University, MA, USA
Arts Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Psychology-Informed Live-Commerce Analytics: Structural Modeling, Predictive Validation, and Governed Script Scoring for TikTok Shopping Xiaochen Li; Yinchen Shi; Jason Zhang
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i1.568

Abstract

This paper examines the psychological factors associated with impulsive-purchase propensity in TikTok Live Commerce and translates those associations into an auditable analytics workflow. It presents a secondary analysis of a public survey workbook containing 415 Indonesian responses on Social Attraction, fear of missing out, Narrative Involvement, Telepresence, Parasocial Interaction, Social Presence, and Impulsive Purchase. The analysis combines measurement diagnostics, a bootstrapped composite structural model, repeated cross-validated classification and continuous prediction, calibration assessment, a coefficient-based illustrative scenario analysis, and external validation of an ethical microcopy screen. In the full sample, Narrative Involvement has the largest association with Parasocial Interaction (β = .516) and Social Presence (β = .496); Parasocial Interaction and Social Presence jointly account for 67.5% of the in-sample variance in Impulsive Purchase. The pattern remains in the quality-screened TikTok-viewer subset (n = 238; Impulsive Purchase R² = .647). For the quality-screened subset, the strongest full-feature classifier is Random Forest (ROC-AUC = .938 ± .031 across 50 held-out folds), while Ridge regression gives the largest mean continuous-outcome R² (.622 ± .114). On 2,356 ec-darkpattern strings, a page-grouped hybrid word/character classifier reaches F1 = .959 ± .011 and ROC-AUC = .989 ± .003. The scenario scores are deterministic implications of the estimated coefficients rather than observed purchase effects. The technical contribution is a governed decision-support architecture that keeps structural association, contemporaneous prediction, scenario prioritization, ethical screening, human review, and prospective testing distinct.
Beyond CTR and VCR: LLM-Assisted Design Evaluation with Eye-Tracking Validation for Graphic Advertising Qiwen Zheng; Xiaochen Li; Fiona Wang
International Journal of Graphic Design Vol. 4 No. 1 (2026): April | IJGD: International Journal of Graphic Design
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/ijgd.v4i1.3994

Abstract

Digital advertising design is often judged with delivery or response metrics such as click-through rate (CTR), video-completion rate (VCR), and viewability, although these measures do not describe whether a banner communicates through a clear visual hierarchy. This study evaluates whether large language model (LLM)-assisted scores and lightweight image features can support graphic advertising review while keeping design-quality prediction distinct from measured visual attention. GraphicDesignEvaluation provided 1,200 principle–image rating rows for alignment, overlap, and white space. Under grouped five-fold cross-validation by base design, the GPT-only Ridge model reached R2 = 0.408, RMSE = 1.566, MAE = 1.257, Pearson = 0.639, and Spearman = 0.634. Direct GPT–human agreement was uneven: overlap was strong (Spearman = 0.769), white space was moderate (0.637), and alignment was weaker (0.519). An external construct analysis used 1,000 advertisements from ADD1000 with aggregate human fixation-density maps and matched EAID ratings. A combined gradient and local-contrast proxy modestly exceeded a center prior (CC = 0.457, SIM = 0.562, KL divergence = 0.637), while lower fixation entropy was associated with higher aesthetic ratings (Spearman = -0.319). BannerRequest400 was used only to describe brief, CTA-language, logo, and format constraints because it contains no human design-quality or gaze labels. The findings support a preliminary, human-supervised design-review framework: LLM scores are useful screening evidence, especially for overlap, but they do not replace human creative judgment, eye tracking, or advertising-performance measures.
Co-Authors Fiona Wang Jason Zhang Qiwen Zheng Yinchen Shi