In the field of green product consumption, consumers tend to seek detailed information to assess product efficacy. In recent years, the advent of Generative Artificial Intelligence (GenAI) tools has significantly streamlined consumers’ access to relevant information on green products. However, as emotional factors are decisive in purchase decision-making, existing studies that predominantly focus on rational decision-making frequently overlook this crucial emotional dimension. Addressing this gap, this study adopts the extended emotion heuristic theory to examine the impact of GenAI tools on green product purchase preferences. Using the partial least squares structural equation model, green product consumption data were collected from multiple regions including Chongqing, Guangdong, Hunan, Hubei, Shanghai, Beijing, and others, between January and March 2025. A total of 717 valid responses were analysed using SPSS 28, Amos 28, and Smart PLS 4.0. The results reveal that certain characteristics of GenAI-generated content—specifically, quality (content relevance, content accuracy), communication style (personalisation, anthropomorphism), and serendipity—positively influence purchase intention for green products. Furthermore, emotional connection plays a partial mediating role. These findings extend the application of emotion heuristic theory in the context of artificial intelligence and highlight the significant role of emotional factors in fostering consumption intentions via GenAI tools. The results offer insights for green product marketers and GenAI tool developers to enhance content quality, communication methods, and additional functions, while also informing regulatory policymaking related to GenAI tools.

Graph prompt learning represents a novel paradigm aimed at enhancing the performance of graph learning models on a variety of downstream tasks by providing specific graph prompts. Despite its promise, current graph prompt learning methods are limited by the following limitations. On the one hand, existing methods often rely on manually selected graph information or simple learnable vectors, which can introduce human biases and lack expressiveness. These methods also fall short in guiding models to induce historical prior knowledge and improve generalization. Furthermore, the direct end-to-end tuning strategy of prompts lacks a necessary gentle transition, which impacts model stability and generalization. To overcome these limitations, we introduce the generalizable graph prompt learning framework (GGPL), which incorporates model-level prompt injection and a two-stage prompt tuning strategy. GGPL focuses on encoding subgraph structures and attributes during pre-training and uses SimGRACE to predict subgraph similarities, enhancing the base model's generalization. The model-level prompt injection module, with its prompt embedding backbone and self-prompt generation, seamlessly integrates invariant knowledge. Our two-stage tuning strategy, including transition and task-specific tuning, ensures better guidance and stability. By designing learnable prompt tokens and fine-tuning them with task-specific information, GGPL enables the model to generalize more robustly to downstream tasks. We conduct extensive experiments on six benchmark datasets to verify the model's effectiveness.

Deep learning models are often trained on datasets that are limited in size and distribution, which may not fully represent the entire range of data encountered in practice. Thus, making deep learning models generalize to out-of-distribution data has received a significant amount of attention in recent studies due to the critical importance of this ability in real-world applications. Meta learning as an effective knowledge transfer paradigm, which learns a base model with high generalization ability to adapt to new data distributions by minimizing domain shifts across tasks during meta-training. However, most existing meta learning methods assume that the base model can access the labels of different domains, and this assumption is demanding in many real application scenarios. In addition, these methods focus on narrowing data-level domain shifts, while ignoring task-level domain shifts, which may lead to inadequate or even negative transfer. Inspired by human learners who use induction to learn and master new tasks, we propose a novel domain-aware meta learning framework for out-of-distribution generalization, termed SMLG. This framework enables the base model to generalize effectively to unseen domains without relying on domain-specific labels. Specifically, we develop a domain-aware transformation module to obtain meta representation and pseudo domain labels. As a result, the base model can be trained robustly without the need for direct domain label input. Furthermore, to investigate the impact of domain shifts at different levels, we introduce a joint loss function that combines cross-entropy with a domain alignment constraint. Extensive experiments on benchmark datasets demonstrate the efficacy of our framework.