Sun Luping, Tang Yanfei
Business School, Central University of Finance and Economics, Beijing, China.
Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China.
Front Psychol. 2021 Sep 30;12:748765. doi: 10.3389/fpsyg.2021.748765. eCollection 2021.
With the development of consumer-centric data collection, storage, and analysis technologies, there is growing popularity for firms to use the behavioral data of individual consumers to implement data-driven discrimination strategies. Different from traditional price discrimination, such data-driven discrimination can take more diverse forms and often discriminates particularly against firms' established customers whom firms know the best. Despite the widespread attention from both the academia and the public, little research examines how consumers react to such discrimination enabled by big data. Based on attribution theory, this paper examines how different ways of consumer attribution of data-driven discrimination influence perceived fairness and consumer trust toward the firm. Specifically, we hypothesize that controllability by consumers and locus of causality of data-driven discrimination interactively influence perceived fairness, which further affects consumer trust. We conduct two experiments to test the hypotheses. Study 1 uses a 2(controllability: high vs. low)×2(locus of causality: internal vs. external) between-subjects design. The results show a significant interaction between controllability and locus of causality on consumer trust. When consumers attribute data-driven discrimination to themselves (internal attribution), consumer trust is significantly lower in low-controllable situations than that in high-controllable situations. When consumers attribute the discrimination to the firm (external attribution), however, the impact of controllability on consumer trust is nonsignificant. Moreover, we show that perceived fairness plays a mediating role in the interaction effect of controllability and locus of causality on consumer trust. Study 2 uses a similar design to replicate the findings of Study 1 and further examines the moderating role of consumer self-concept clarity. The results show that the findings of study 1 apply only to consumers with low self-concept clarity. For consumers with high self-concept clarity, regardless of the locus of causality (internal or external), consumer trust is significantly higher in high-controllable situations than that in low-controllable situations. Finally, we discuss the theoretical and managerial implications and conclude the paper by pointing out future research directions.
随着以消费者为中心的数据收集、存储和分析技术的发展，企业利用个体消费者的行为数据来实施数据驱动的歧视策略越来越普遍。与传统的价格歧视不同，这种数据驱动的歧视可以采取更多样化的形式，并且往往特别针对企业最了解的老客户进行歧视。尽管学术界和公众都广泛关注，但很少有研究考察消费者对大数据带来的这种歧视会作何反应。基于归因理论，本文考察了消费者对数据驱动歧视的不同归因方式如何影响其对企业的公平感认知和信任。具体而言，我们假设消费者的可控性和数据驱动歧视的因果 locus 会交互影响公平感认知，进而影响消费者信任。我们进行了两项实验来检验这些假设。研究 1 采用 2（可控性：高 vs. 低）×2（因果 locus：内部 vs. 外部）被试间设计。结果表明，可控性和因果 locus 对消费者信任有显著的交互作用。当消费者将数据驱动的歧视归因于自己（内部归因）时，在低可控性情况下的消费者信任显著低于高可控性情况下的消费者信任。然而，当消费者将歧视归因于企业（外部归因）时，可控性对消费者信任的影响不显著。此外，我们表明公平感认知在可控性和因果 locus 对消费者信任的交互作用中起中介作用。研究 2 采用类似设计来复制研究 1 的结果，并进一步考察消费者自我概念清晰度的调节作用。结果表明，研究 1 的结果仅适用于自我概念清晰度低的消费者。对于自我概念清晰度高的消费者，无论因果 locus（内部或外部）如何，在高可控性情况下的消费者信任显著高于低可控性情况下的消费者信任。最后，我们讨论了理论和管理意义，并通过指出未来的研究方向来结束本文。
