(1) Background: AI technology has been deeply applied to online shopping platforms to provide more accurate and personalized services for consumers. It is of great significance to study the different functional experiences of AI for consumers to improve the current application status of AI technology. (2) Method: Based on the “SOR” model, this study divides the AI technology experienced by the consumers of online shopping platforms into three dimensions: accuracy, insight, and interaction experience. The perceived value is taken as the mediating variable from the prospect of perceived utility value and perceived hedonic value. This article uses empirical research methods to analyze the effect of the three dimensions of online shopping AI experience to research the internal influence mechanism of consumers’ purchase intention. (3) Results: 1. The accuracy, insight, and interaction experience of AI marketing technology each have a significant positive impact on consumers’ perceived utility value and hedonic value; 2. Both the perceived utility value and perceived hedonic value obtained by an AI technology experience can promote the formation of consumers’ purchase intention; 3. The perceived hedonic value was better than perceived utility value at promoting the consumers’ purchase intention.

AI-personalized recommendation technology offers more accurate and diverse choices to consumers and increases click-through rates and sales on e-commerce platforms. Yet, data on consumers’ experiences of AI-personalized recommendations and their impact path on clicking intention are scarce. This article addressed these issues through three studies. In study 1, we adopted the Grounded Theory approach to conduct in-depth interviews with 30 Chinese consumers and constructed a scale to measure the impact of consumer experience on click intention. In study 2, we adopted the empirical research method to conduct reliability and validity tests on 347 valid questionnaires to finalize the scale officially. In study 3, based on the SOR theory, we constructed a model and formulated hypotheses and then conducted empirical analysis using 1097 valid questionnaires. We found that the relevance, inspiration, and insightful experiences of AI-personalized recommendations can significantly promote consumers’ clicking intention. Moreover, immersive experience mediates between the former three factors and clicking intention, and technology acceptance mediates between relevance, inspiration, and clicking intention. When consumers perceive a high degree of information privacy infringement, the immersive experience’s positive impact on clicking intention will be weakened. Meanwhile, the promoting effect of technology acceptance on clicking intention will also be inhibited. When information quality improves, the positive impact of technology acceptance on clicking intention will be enhanced. This research fills the gap in the literature on consumers’ experiences of AI-personalized recommendations and clarifies how these experiences affect the clicking intention. It offers valuable insights for e-commerce platforms to continuously optimize personalized recommendation algorithms and boost the click conversion rate of online shopping.

This study focuses on the factors influencing consumers’ continuous participation willingness in online pre-sale activities. Based on perceived value and social exchange theory, it analyzes how perceived benefits (including practical benefits, hedonic benefits, and social benefits) and perceived costs (including search costs, waiting costs, and adjustment costs) affect consumer satisfaction and their continuous participation intention in online pre-sales. A total of 527 valid questionnaires were collected, and structural equation modeling was used for data analysis. The results indicate that perceived benefits are significantly positively correlated with consumer satisfaction and their willingness to continue participating, while perceived costs are significantly negatively correlated with both aspects. Additionally, the study found that product type moderates the effect of perceived benefits and perceived costs on consumer satisfaction. This research helps retailers understand consumers’ willingness to continuously participate in online pre-sales and provides valuable insights for developing pre-sale strategies.

AI personalized recommendation technology has been deeply applied to online shopping, but there is a lack of research on consumer experience of using AI personalized recommendation with the online shopping click intention. This study focused on the impact path of consumer relevance experience in AI personalized recommendation on consumers' online click intention when shopping online. We used SPSS26.0 to analyze the consumer data from 1097 consumers’ questionnaires. We found that relevance experiences can positively promote online click intention through flow experiences. Privacy and security violations can regulate the impact of flow experience on online click intention. The stronger consumers' perception of privacy and security violations in AI personalized recommendations, the weaker the impact of flow experience on online click intention. This study provides the direction for improving AI personalized recommendation algorithms, which should promote consumers to obtain more relevant experiences, thereby promoting flow experiences, enhancing consumers' online click intention, and promoting the increase of sales on online shopping platforms.