Digital divides in the AI era: Socioeconomic stratification and the dual role of digital payments in China

Yifei Lü

International Sociology2026https://doi.org/10.1177/02685809251414400article
AJG 2
Weight
0.50

What the paper says

This study examines how socioeconomic stratification and prior technology practice jointly shape artificial intelligence (AI) awareness in China. We theorize a practice-to-cognition mechanism in which routine, AI-embedded digital payments cultivate perceived usefulness and mastery, thereby translating structural position into cognitive orientation toward AI. Integrating digital divide scholarship with Technology Acceptance and self-efficacy perspectives, we treat digital payment use as both a mediator that transmits the effects of social position and a moderator that conditions those effects across strata. Using nationally representative 2023 China Household Finance Survey (CHFS) data and logistic, moderation, and causal mediation analyses, we show that education, income, and hukou and residence status are significant predictors of AI awareness; digital payment practice mediates about 14%–15% of these associations and systematically reshapes their magnitudes. The same practice that can buffer disadvantage also consolidates advantage by reinforcing AI self-efficacy, revealing a layered dynamic in which everyday platform interactions can bridge yet also reproduce cognitive inequalities. The study advances theory by formalizing the dual-pathway model linking social structure, technological practice, and cognition, demonstrating how mundane, accumulated use of incumbent technologies scaffolds awareness of emergent ones, and offering practical guidance for inclusive AI engagement.

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https://doi.org/https://doi.org/10.1177/02685809251414400

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@article{yifei2026,
  title        = {{Digital divides in the AI era: Socioeconomic stratification and the dual role of digital payments in China}},
  author       = {Yifei Lü},
  journal      = {International Sociology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/02685809251414400},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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