For Approval Current Psychology https://doi.org/10.1007/s12144-025-07456-0 et al., 2023). Many studies have considered (a) algorithms- based models, (b) customer purchase history and data, (c) digital environments, (d) digital content, and (e) information technology infrastructure (Malthouse & Copulsky, 2023), trust and acceptance of AI recommendations (Kim et al., 2021), and personlized AI systems (Shin, 2020). However, a few studies have focused on Taobao’s online platforms’ AI shopping recommendations. Accordingly, this research filled the gap in AI-based personalized service satisfaction. It addressed the relationship between AI products and price recommendation accuracy based on AI for the customer purchase process. Behavioral reasoning theory-based study encourages us to examine how customers use or resist ubiquitous AI-related digital transformation tools for customer-company interac- tions (Jan et al., 2023). Through comparative analysis, the stimulus organism response (S-O-R) model confirmed that digital multisensory cues connect AI-based digital assistance and customer engagement. Customers expect that purchase decision-related risks or benefits depend on digital sources. Introduction Digital innovations have transformed consumer purchases and business practices (Nazir et al., 2023). Recent research confirmed personalized shopping and AI virtual assistants’ role in e-commerce’s growth in innovation (Kamoonpuri and Sengar, 2023). They increased the level of intelligence in the context of brands’ purchase decisions (Sestino, 2024). The dynamic capability and productivity paradox theories demonstrated customer relationship management through AI-enabled applications boosted service innovation (Kumar  Rita Yi Man Li ymli@hksyu.edu Yue Shan shanyue1289@163.com 1 Department of Economics and Finance, Hong Kong Shue Yan University, 10 Wai Tsui Cres, North Point, Hong Kong, China Abstract This study investigated the impact of AI price and product recommendation accuracy on customer satisfaction based on the stimulus-organism response (SOR) theory. Analysing online users’ surveys through PLS-SEM, this study found that brand sensitivity significantly moderates the relationship between AI recommendation's product accuracy and personal- ized service satisfaction but not the link between price accuracy and personalized service satisfaction. Investment strategy exhibited an insignificant moderating effect on the relationship between personalized service satisfaction, product and price accuracy. The results revealed that accurate price and product recommendation directly impact personalized service satisfaction. This is the first study to examine AI recommendation’s effects on personalized service satisfaction with the multi-moderation of investment strategy and brand sensitivity. The findings extend the SOR framework in literature to the AI-powered online shopping. They provide practical information for e-commerce providers to enhance customer satisfaction by adopting AI technologies that accurate recommend price and product to their potential customers. Policy makers may implement related policies to enhance AI price and product recommendation transparency by encouraging the usage of explainable artificial intelligence and providing more education to customers about AI recommendations in online shopping platforms. Keywords Personalised services satisfaction · AI recommendation · Online shopping platform · Electronic commerce · And brand sensitivity Accepted: 27 January 2025 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025 The impact of AI recommendation’s price and product accuracy on customer satisfaction: SEM-SOR theoretical approach Yue Shan 1  · Rita Yi Man Li 1 1 3