Digital Commerce and the Digitally Connected Enterprise
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Paper Type
Complete
Paper Number
1537
Description
How far should firms invest in their personalized recommendation mechanisms, and whether all personalized recommendations are equally welcomed by online consumers? To answer this question, we investigate users’ perceptions of three types of personalized recommendations: One-to-All, One-to-Many, and One-to-One, through the lens of Resource Matching Theory. Using both experiments and configurational analysis approach, our study posits that online consumers experience each type of personalized recommendation and their resource matching sources (Familiarity, Complexity, External Information) simultaneously and differently in various shopping contexts. We further document the evidence that the most personalized recommendation One-to-One is not always perceived useful as conventionally believed. Our study abductively formulates three theoretical propositions regarding the usefulness of each personalized recommendation. In addition, we propose e-commerce vendors should consider three resource matching dimensions including Familiarity, Complexity, and External Information to avoid collecting more-than-enough customer data and adequately personalize recommendation results on their online digital platforms.
Recommended Citation
Nguyen, Tuan (Kellan) and Hsu, Pei-fang, "More Personalized, More Useful? Reinvestigate Recommendation Mechanisms in e-Commerce" (2020). ICIS 2020 Proceedings. 4.
https://aisel.aisnet.org/icis2020/digital_commerce/digital_commerce/4
More Personalized, More Useful? Reinvestigate Recommendation Mechanisms in e-Commerce
How far should firms invest in their personalized recommendation mechanisms, and whether all personalized recommendations are equally welcomed by online consumers? To answer this question, we investigate users’ perceptions of three types of personalized recommendations: One-to-All, One-to-Many, and One-to-One, through the lens of Resource Matching Theory. Using both experiments and configurational analysis approach, our study posits that online consumers experience each type of personalized recommendation and their resource matching sources (Familiarity, Complexity, External Information) simultaneously and differently in various shopping contexts. We further document the evidence that the most personalized recommendation One-to-One is not always perceived useful as conventionally believed. Our study abductively formulates three theoretical propositions regarding the usefulness of each personalized recommendation. In addition, we propose e-commerce vendors should consider three resource matching dimensions including Familiarity, Complexity, and External Information to avoid collecting more-than-enough customer data and adequately personalize recommendation results on their online digital platforms.
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