## 1 Introduction  
Promoting green purchasing behavior has become a crucial step in a country's sustainable development [1]. In the past decade, consumers' demand for green products has increased significantly [2]. The widespread popularity of online shopping in various countries has provided a broad development platform for the online purchase of sustainable products [3]. Empirical studies indicate that social media [4, 5] and social e-commerce platforms with integrated e-commerce functions are widely utilized to promote green consumption behavior [6]. Although e-commerce has reduced the geographical barriers to green products and met consumers' needs for ecological protection [7], it has also exacerbated information overload and choice complexity. As a result, most consumers still have doubts about purchasing sustainable products online, which may lead consumers to miss the opportunity to make more sustainable choices [8]. Notably, the latest advancements in large language models (LLMs) present a potential solution. LLMs demonstrate excellence in consumer demand response capabilities and operational efficiency and are increasingly emerging as a key enabling tool in the field of online shopping [9]. Specifically, new technologies, such as LLMs, can present product attribute summaries, alternative comparisons, and personalized services in real time in a conversational format, assisting users in narrowing the cognitive gap and reducing search costs [10]. This empowers them to acquire more information about online sustainable products and access more environmentally friendly options [1,11]. Existing research indicates that LLMs can effectively change participants' consumption intentions and pre-existing beliefs [12]. However, [PERSON] et al. [13] pointed out that the influence of LLMs may have certain limitations. These controversial findings further highlight the need to deeply explore the influence mechanism of LLM technology acceptance. Furthermore, although LLM technology has great recommendation potential [14], the acceptance mechanism of sustainable consumers regarding this new method of obtaining shopping information remains under-researched. Therefore, understanding the key factors shaping users' adoption of LLMs in online purchasing holds significant theoretical and practical implications [15]. There exist substantial disparities in the motivation for technology adoption and purchasing behavior between two distinct user groups: the LLM user group and the green consumption user group [16,17]. The university student group is not only a large user group of LLM tools [18] but also tends to be more active in environmental protection [19]. However, existing research on university students' use of LLM assistance mainly focuses on the educational field [20], and there is a significant lack of research on its influence mechanism in online green consumption decisions. In particular, there are still many unsolved questions about how LLMs reshape the green consumption experience. Therefore, this study focuses on this group, aiming to bridge the knowledge gap regarding how LLM technology influences young consumers' environmentally friendly shopping decisions and technology utility. The most prominent model utilized to explain an individual's technology adoption and utilization is the technology acceptance model (TAM) [21]. This model postulates that the influence of all external variables on behavior is mediated by perceived usefulness (PU) and perceived ease of use (PEU) [22]. Although the explanatory power of TAM has been well-established, its over-emphasis on functional value may lead to overlooking broader evaluation criteria. Users also assess the overall benefits and cost sacrifices associated with a technology, namely, its perceived value. Prior research has indicated that perceived value is a decisive driving factor for the adoption of the technology in diverse fields [23].