Optimal dynamic pricing strategy for non-durable experience goods: the role of consumer AI learning.
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| Title: | Optimal dynamic pricing strategy for non-durable experience goods: the role of consumer AI learning. |
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| Authors: | Li, Zhitang1 (AUTHOR), Lev, Benjamin2 (AUTHOR) bl355@drexel.edu |
| Source: | International Journal of Production Research. Dec2025, Vol. 63 Issue 24, p10700-10724. 25p. |
| Subjects: | Perceived quality, Consumer behavior, Economic demand, Time-based pricing, Revenue management |
| Abstract: | This study develops a dynamic pricing model for experiential products by explicitly integrating product perceived quality and AI-driven consumer learning. Several interesting and key conclusions are obtained. First, the results demonstrate that companies strategically adjust pricing in response to the strength of AI-enabled information cascades and the perceived quality signals after AI learning. Moreover, AI-driven consumer learning significantly shapes both optimal pricing and revenue. Specifically, consumer AI learning has a significant impact on both pricing and revenue gains. When the information cascade is in a positive state and the perceived quality indicator after AI learning is positive, it indicates strong market demand, and consumers are willing to accept higher-priced products, allowing companies to adjust prices upwards to increase revenue. In contrast, when the information cascade is in a positive state and the perceived quality indicator after AI learning is negative, it suggests weak market demand, and consumers are unwilling to pay higher prices for products, prompting companies to lower prices to stimulate demand. Furthermore, this study explores when demand fluctuations under consumer AI learning for various products increase, prices should be adjusted downward, highlighting the significant effect of demand changes under AI learning on pricing strategies when facing competition. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Abstract: | This study develops a dynamic pricing model for experiential products by explicitly integrating product perceived quality and AI-driven consumer learning. Several interesting and key conclusions are obtained. First, the results demonstrate that companies strategically adjust pricing in response to the strength of AI-enabled information cascades and the perceived quality signals after AI learning. Moreover, AI-driven consumer learning significantly shapes both optimal pricing and revenue. Specifically, consumer AI learning has a significant impact on both pricing and revenue gains. When the information cascade is in a positive state and the perceived quality indicator after AI learning is positive, it indicates strong market demand, and consumers are willing to accept higher-priced products, allowing companies to adjust prices upwards to increase revenue. In contrast, when the information cascade is in a positive state and the perceived quality indicator after AI learning is negative, it suggests weak market demand, and consumers are unwilling to pay higher prices for products, prompting companies to lower prices to stimulate demand. Furthermore, this study explores when demand fluctuations under consumer AI learning for various products increase, prices should be adjusted downward, highlighting the significant effect of demand changes under AI learning on pricing strategies when facing competition. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00207543 |
| DOI: | 10.1080/00207543.2025.2555533 |
