Data-Driven Discrimination, Perceived Fairness, and Consumer Trust–The Perspective of Consumer Attribution
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Psychology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.748765/full |
| _version_ | 1818865647003631616 |
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| author | Luping Sun Yanfei Tang |
| author_facet | Luping Sun Yanfei Tang |
| author_sort | Luping Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T10:50:52Z |
| format | Article |
| id | doaj.art-9e5a036cf8c54d44a3f0f2158b496bc1 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-12-19T10:50:52Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-9e5a036cf8c54d44a3f0f2158b496bc12022-12-21T20:25:01ZengFrontiers Media S.A.Frontiers in Psychology1664-10782021-09-011210.3389/fpsyg.2021.748765748765Data-Driven Discrimination, Perceived Fairness, and Consumer Trust–The Perspective of Consumer AttributionLuping Sun0Yanfei Tang1Business School, Central University of Finance and Economics, Beijing, ChinaAntai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, ChinaWith 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.https://www.frontiersin.org/articles/10.3389/fpsyg.2021.748765/fulldata-driven discriminationperceived fairnessconsumer trustattributionconsumer self-concept clarity |
| spellingShingle | Luping Sun Yanfei Tang Data-Driven Discrimination, Perceived Fairness, and Consumer Trust–The Perspective of Consumer Attribution Frontiers in Psychology data-driven discrimination perceived fairness consumer trust attribution consumer self-concept clarity |
| title | Data-Driven Discrimination, Perceived Fairness, and Consumer Trust–The Perspective of Consumer Attribution |
| title_full | Data-Driven Discrimination, Perceived Fairness, and Consumer Trust–The Perspective of Consumer Attribution |
| title_fullStr | Data-Driven Discrimination, Perceived Fairness, and Consumer Trust–The Perspective of Consumer Attribution |
| title_full_unstemmed | Data-Driven Discrimination, Perceived Fairness, and Consumer Trust–The Perspective of Consumer Attribution |
| title_short | Data-Driven Discrimination, Perceived Fairness, and Consumer Trust–The Perspective of Consumer Attribution |
| title_sort | data driven discrimination perceived fairness and consumer trust the perspective of consumer attribution |
| topic | data-driven discrimination perceived fairness consumer trust attribution consumer self-concept clarity |
| url | https://www.frontiersin.org/articles/10.3389/fpsyg.2021.748765/full |
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