The Paradox of Algorithmic Pricing: How “Big Data Price Discrimination” Affects Consumer Trust and Repurchase Intention
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Keywords
algorithmic pricing, big data price discrimination, perceived fairness, consumer trust, repurchase intention
Abstract
With the rapid development of e-commerce and the sharing economy platforms, algorithmic pricing has evolved from a macro-level market supply and demand adjustment tool into a micro-level “perfect price discrimination” based on individual consumer behavior data, also known as “big data price discrimination.” This paper systematically reviews the profound impact of big data price discrimination on consumer psychology and behavior. This study points out that most companies face a core “pricing paradox”: while algorithmic pricing can maximize a company's economic surplus in a single transaction, this behavior, which violates traditional pricing norms, triggers strong consumer perceptions of unfairness. Literature indicates that consumers' negative attributions to data misuse directly destroy their cognitive and emotional trust in the platform, leading to a precipitous decline in platform satisfaction and repurchase intention. This paper reveals the specific mechanisms of this trust loss by comparing unpredictable price fluctuations with behavior-based user profiles, and provides management insights for companies on how to design fair pricing in the algorithmic era.
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