With the rapid adoption of generative artificial intelligence (GenAI) chatbots on e-commerce platforms, users’ expectations for anthropomorphic service experiences have risen significantly. Despite the growing presence of GenAI, little is known about how different types of anthropomorphic users’ self-efficacy and the intention to adopt as a decision aid through distinct cognitive pathways. Addressing this research gap, this study draws on the elaboration likelihood model (ELM) to develop a comprehensive framework that integrates central and peripheral cues. Using large-scale survey data from e-commerce users and structural equation modeling, the research empirically examines the mediating role of self-efficacy and the moderating effect of information overload. Results indicate that human-like empathy and perceived warmth (peripheral cues) and perceived competence (central cue) all significantly enhance self-efficacy, which in turn positively influences the intention to adopt as a decision aid. Moreover, information overload intensifies the effect of peripheral cues on self-efficacy but has a limited impact on central cues. These findings advance the theoretical understanding of GenAI–human interaction by clarifying the mechanisms through which anthropomorphic features operate, and provide actionable insights for designing user-centric GenAI recommendation services to optimize user experience and encourage the intention to adopt as a decision aid in e-commerce.

This review synthesizes how Life Cycle Assessment (LCA) and Techno-Economic Assessment (TEA) can be jointly used to guide choices for converting diverse nuclear residues into durable final forms suitable for storage and geological disposal across national program contexts. It contrasts established options, including borosilicate glass vitrification for High Level Waste (HLW) and cementation for Intermediate Level Waste (ILW) and Low Level Waste (LLW), with emerging candidates such as Synroc type ceramics, chemically bonded phosphate ceramics, and alkali activated geopolymers. Across these options, the review shows that apparent environmental and cost rankings are often artifacts of modeling choices, especially functional unit selection and the inclusion of repository construction, emplacement, and stewardship within system boundaries. Methodological debates are therefore treated as first order technical issues, not reporting details, covering attributional versus consequential modeling, allocation for by product derived feedstocks, and the representation of long horizon radiological impacts. A central finding is that long-term durability, captured through performance assessment concepts and laboratory leaching metrics, remains the dominant source of epistemic uncertainty, frequently overwhelming material to material differences and propagating into both LCA results and regulatory cost risk in TEA. To improve decision relevance, the review advocates routine probabilistic sensitivity analysis, explicit reporting of boundary cases, and integrated workflows that couple process engineering inventories with near field geochemistry scenarios and finance modules. Finally, it positions multi criteria decision analysis as a practical bridge for stakeholders to examine trade offs among volume reduction, energy demand, maturity, and qualification burden under transparent value weightings.