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Paper Number
2226
Paper Type
short
Description
Past studies on the long-tail phenomenon in digital markets or platforms have examined how buyer-side technologies affect the sales distribution of products. However, these studies did not fully explain how emerging platform-developed AI pricing agents, as a seller-side technology, can affect the sales distribution of products on e-commerce platforms. To fill this gap, we use a proprietary dataset from an e-commerce platform in Asia to empirically quantify the effect of sellers’ adoption of platform-developed AI price recommendations on the aggregate sales distribution of products. By employing a Heckman-type model for product-level analysis, we find that AI pricing adoption leads to a more diverse sales distribution of products, and this effect is accentuated when market competition is more intense. Our category-level analysis affirms this finding. Mechanism evaluations reveal products’ prices recommended by AI pricing agents help improve sellers’ product visibility, conversation rate, and average quantity purchased.
Recommended Citation
Zhang, Xinyi; Goh, Khim Yong; and Sun, Chenshuo, "The Effect of Platform-developed AI Price Recommendations Adoption on E-Commerce Platform Sales Distribution" (2023). ICIS 2023 Proceedings. 11.
https://aisel.aisnet.org/icis2023/emobilecomm/emobilecomm/11
The Effect of Platform-developed AI Price Recommendations Adoption on E-Commerce Platform Sales Distribution
Past studies on the long-tail phenomenon in digital markets or platforms have examined how buyer-side technologies affect the sales distribution of products. However, these studies did not fully explain how emerging platform-developed AI pricing agents, as a seller-side technology, can affect the sales distribution of products on e-commerce platforms. To fill this gap, we use a proprietary dataset from an e-commerce platform in Asia to empirically quantify the effect of sellers’ adoption of platform-developed AI price recommendations on the aggregate sales distribution of products. By employing a Heckman-type model for product-level analysis, we find that AI pricing adoption leads to a more diverse sales distribution of products, and this effect is accentuated when market competition is more intense. Our category-level analysis affirms this finding. Mechanism evaluations reveal products’ prices recommended by AI pricing agents help improve sellers’ product visibility, conversation rate, and average quantity purchased.
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