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Paper Type
ERF
Description
Recommendation systems (RSs) are artificial intelligence (AI) algorithms that utilize customers’ product interests or preferences in their response to product search requests. However, AI-powered RSs have been investigated for perpetuating biases. This research extends the theoretical understanding of how customers perceive the fairness of RSs and the factors that impact this perception. Although equity theory has been studied in the management literature to explain the variations in individuals’ reactions towards (un)fairness of managerial and organizational decisions, its effect on the perceived fairness of AI systems such as RSs has yet to be studied. Our study extends the extant research by 1) examining how customers’ sensitivity to equity impact their perceived fairness of RSs and 2) how the perceived fairness of such systems influences customers’ satisfaction, attitudinal, and behavioral loyalties toward e-commerce platforms recommendation systems. Further, our study will help e-commerce managers better understand customers’ psychological profiles and predict their responses to RSs (un)fairness.
Paper Number
1666
Recommended Citation
Sharma, Sachin; Singh, Vivek Kumar; and Joshi, Kailash, "Fairness of E-Commerce Platform Product Recommendations: Understanding Customers’ Perceived Fairness and Equity Sensitivity" (2023). AMCIS 2023 Proceedings. 18.
https://aisel.aisnet.org/amcis2023/sig_odis/sig_odis/18
Fairness of E-Commerce Platform Product Recommendations: Understanding Customers’ Perceived Fairness and Equity Sensitivity
Recommendation systems (RSs) are artificial intelligence (AI) algorithms that utilize customers’ product interests or preferences in their response to product search requests. However, AI-powered RSs have been investigated for perpetuating biases. This research extends the theoretical understanding of how customers perceive the fairness of RSs and the factors that impact this perception. Although equity theory has been studied in the management literature to explain the variations in individuals’ reactions towards (un)fairness of managerial and organizational decisions, its effect on the perceived fairness of AI systems such as RSs has yet to be studied. Our study extends the extant research by 1) examining how customers’ sensitivity to equity impact their perceived fairness of RSs and 2) how the perceived fairness of such systems influences customers’ satisfaction, attitudinal, and behavioral loyalties toward e-commerce platforms recommendation systems. Further, our study will help e-commerce managers better understand customers’ psychological profiles and predict their responses to RSs (un)fairness.
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