Technical–Emotional Configurations and Platform Heterogeneity of Purchase Behavior Tendencies in AI Digital-Human Livestreaming: Based on the SOR–PAD Framework

AI digital-human livestreaming has become a key format of e-commerce livestreaming due to its low cost and round-the-clock operation. How the technical features of digital humans work together with consumers’ psychological perceptions to drive consumption is an important academic and practical question. Existing studies mostly adopt linear analytical paradigms. They focus on the anthropomorphic appearance features of digital humans. Few studies interpret the synergistic effects of technical–emotional factors from a configurational perspective. Empirical tests on boundary conditions under different platform ecosystems are also scarce. Drawing on the SOR–PAD theoretical framework, this study takes 18,690 livestream bullet-screen comments collected from 40 AI digital-human livestream rooms across Jingdong, Baidu, and Meituan as the research material. It conducts a mixed empirical analysis, combining text mining, machine learning, and fsQCA. The dependent variable, purchase behavior tendency, is obtained from an SVM model trained by matching bullet-screen comments with desensitized background transaction data. This indicator acts as a proxy measure for subjective purchase intention. The SVM model achieves an accuracy of 0.97, recall of 0.94, and an F1-score of 0.95. The findings are as follows. First, four technical features, including professionalism, simulation fidelity, responsiveness, and personalization, together with the three psychological perceptions of pleasure, arousal, and trust, constitute critical antecedents of high purchase behavior tendencies. Second, no single necessary condition can trigger a high purchase behavior tendency. Five equivalent technical–emotional configuration paths are identified, including response-oriented, pleasure-oriented, and multiple-synergy types. Third, the conversion effects of the configuration paths show clear platform boundaries. Different e-commerce formats match differentiated configuration paths. This study introduces configurational causal logic into the existing SOR–PAD framework. It expands the application boundary of this framework for AI digital-human livestreaming scenarios. It supplements the empirical evidence of consumer behavior under human–computer interactions. It also offers practical references for platforms and merchants to operate AI digital-human livestreams.