(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.

With the rapid development of artificial intelligence technology, its widespread application in the field of business management has become a significant issue faced by contemporary enterprises. Based on the Technology Acceptance Model, this study explores the impact of AI technology acceptance on organizational decision-making efficiency, performance, and the depth of technology application. It also reveals the driving mechanisms of top management support, perceived usefulness, and perceived ease of use on AI technology adoption through path analysis. To validate the research hypotheses, the study employed structural equation modeling (SEM) based on survey data collected from 420 respondents across various industries. The study found that top management support significantly enhances technology acceptance through perceived variables, while perceived usefulness is the core factor driving technology adoption. Although perceived ease of use has a weaker effect, it is equally important in lowering the psychological barriers during the initial stages of technology adoption. The adoption of AI technology has significantly improved organizational decision efficiency and overall performance, promoting the deep application of technology by optimizing resource allocation and enhancing scientific decision-making capabilities. This study further validates the applicability of the TAM theory in the context of AI technology, expanding its theoretical explanatory power in complex technology-adoption mechanisms. At the same time, the research provides practical guidance for enterprises in the introduction and application of technology, emphasizing that managers need to shape an open and innovative organizational culture at a strategic level and enhance employees’ willingness to accept technology through technical training and value transmission. Future research can incorporate cross-cultural and multi-level analytical frameworks to explore the dynamic adaptation paths of AI technology adoption and its potential risks in sustainable development.

Integrating artificial intelligence (AI) and cause-related marketing has reshaped corporate social responsibility practices while triggering a conflict between technological instrumental rationality and moral value transmission. Building on the Persuasion Knowledge Model (PKM) and AI aversion literature, this research employs two experiments to reveal that AI disclosure exerts a unique inhibitory effect on consumers’ purchase intentions in cause-related marketing contexts compared to non-cause-related marketing scenarios. Further analysis uncovers a chain mediation pathway through consumer skepticism and advertisement attitudes, explaining the psychological mechanism underlying AI disclosure’s impact on purchase intentions. The study also identifies the moderating role of AI aversion within this chain model. The findings provide a new theoretical perspective for integrating AI disclosure, consumer psychological responses, and marketing effectiveness while exposing the “value-instrumentality” conflict inherent in AI applications for cause-related marketing. This research advances the evolution of the PKM in the digital era and offers practical insights for cause-related marketing enterprises to balance AI technology application with optimized disclosure strategies.

At present, China’s cross-border e-commerce has ushered in a golden period of development. When developing cross-border e-commerce, enterprises should first assess the market climate of the target country and reasonably select the target country. Based on the PESTEL theory, an evaluation index system is established for China’s cross-border e-commerce overseas strategic climate. Taking “One Belt, One Road” as the opportunity and background, the overseas strategic climate of cross-border e-commerce in 62 countries along the “One Belt, One Road” is selected as the research object, and the Decision Tree and Adaptive Boosting classification methods in machine learning are applied to train and predict the established index system. Finally an overall picture of the overseas strategic climate of the 62 countries is obtained. The results are compared and analysed in depth to identify the most suitable countries for cross-border e-merchants and to provide reference for cross-border e-merchants investors.

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.