Information Systems Faculty Publications and Presentations
Document Type
Article
Publication Date
12-2024
Abstract
The increasing use of artificial intelligence (AI) to enhance products and services has enabled personalized offerings and smarter functionalities through the analysis of consumer data. However, privacy concerns present significant challenges to the effective utilization and commercialization of AI-enabled products. To address these concerns, firms must carefully navigate consumer data privacy and develop appropriate data collection strategies to support future product intelligence, particularly with AI technologies like ChatGPT. This study examines two primary data collection approaches: the uniform policy strategy and the option menu strategy. A mathematical model is constructed to assess these strategies, considering factors such as information externalities and heterogeneous consumer privacy concerns. By comparing firm profits, consumer surplus, and social welfare under both strategies, the study finds that the option menu strategy becomes optimal when there are considerable differences in privacy concerns across consumer groups or when even smaller differences exist, but consumers place a high value on personalized services. These insights offer guidance to firms and policymakers in formulating appropriate data collection strategies for AI-enabled products.
Recommended Citation
Yang, Z., Li, Y., Sun, J., Hu, X. and Zhang, Y., 2024. Consumer private data collection strategies for AI-enabled products. Electronic Commerce Research and Applications, 68, p.101460. https://doi.org/10.1016/j.elerap.2024.101460
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Title
Electronic Commerce Research and Applications
DOI
10.1016/j.elerap.2024.101460
Comments
Original published version available at https://doi.org/10.1016/j.elerap.2024.101460