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Unveiling big data-driven price discrimination in China: Evidence from public opinion on Chinese Tiktok

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  • Xing, Yunfei
  • He, Yuming
  • Zeng, Luona

Abstract

The application of advanced artificial intelligence (AI) algorithms in China's big data-driven price discrimination (BDPD) has garnered considerable attention due to its significant impact on consumer welfare and market efficiency. These algorithms enable dynamic pricing adjustments, yet their lack of transparency often leads to public perceptions of inequity and exploitation. This paper presents a case study of Douyin, a prominent Chinese social media platform, to examine public opinion regarding BDPD. Leveraging established AI ethics frameworks, we conduct an inductive qualitative content analysis to identify the ethical principles pertinent to various facets of BDPD. A taxonomy is developed based on four primary AI biases expressed by the Chinese public regarding BDPD: (1) Price increases following search experiences; (2) Price variations across different devices; (3) Discriminatory pricing based on customers' consumption levels; and (4) Customer tagging for discriminatory information delivery. Furthermore, we construct a theoretical framework that informs future research and provide actionable insights for scholars. The results support policy development aimed at addressing public apprehensions about the ethical ramifications of emerging technologies and algorithmic systems, thereby promoting mutually beneficial outcomes.

Suggested Citation

  • Xing, Yunfei & He, Yuming & Zeng, Luona, 2026. "Unveiling big data-driven price discrimination in China: Evidence from public opinion on Chinese Tiktok," Technological Forecasting and Social Change, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:tefoso:v:223:y:2026:i:c:s0040162525004767
    DOI: 10.1016/j.techfore.2025.124445
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