Abstract

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.

Highlights

  • Due to the advancement of data storage technologies and big data analytics, increasingly more firms have been tracking and analyzing individual consumers’ online shopping behaviors

  • This research employs attribution theory to investigate how consumers respond to a new type of discrimination that emerges with the development of big data analytics – data-driven discrimination

  • The nonsignificant impact of locus of causality in our research setting could be caused by a ceiling effect, in which data-driven discrimination generates extremely unfair perceptions

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Summary

Introduction

Due to the advancement of data storage technologies and big data analytics, increasingly more firms have been tracking and analyzing individual consumers’ online shopping behaviors (e.g., online search, browsing, and purchasing). Data-driven discrimination may especially discriminate against firms’ established customers whom firms know the best, the practice of which has been known as loyalty penalty (Parker, 2021). It differs from personalized marketing or loyalty program, which is used by firms to better serve their loyal customers through analyzing customer behavioral data (Stourm et al, 2020). This may be the reason why data-driven discrimination has raised widespread controversy among the public. It is no wonder that increasingly more consumers report being discriminated by firms’ algorithms

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