Psychology-Informed Live-Commerce Analytics: Structural Modeling, Predictive Validation, and Governed Script Scoring for TikTok Shopping

Xiaochen  Li; Yinchen  Shi; Jason  Zhang

doi:10.51903/jtie.v5i1.568

Authors

Xiaochen Li Marketing Analytics, Bentley University, MA, USA
Yinchen Shi Computer Science, New York University, NY, USA
Jason Zhang Computer Science, Cornell Tech, NY, USA

DOI:

https://doi.org/10.51903/jtie.v5i1.568

Keywords:

TikTok Live Commerce, impulsive purchase, FOMO, Structural Modeling, Cross-Validation

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.

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