Exploring consumer perceptions of AI-generated eco-recommendations:a qualitative comparison between the UK and China
Year: 2025
Type: conference-paper
Abstract: As artificial intelligence (AI) becomes increasingly integrated into consumer decision-making processes, its potential to promote sustainable behaviours has attracted a lot of attention across industries and academia. AI-generated eco-recommendations has been regarded as a promising tool to encourage environmentally friendly consumption choices. However, existing studies have focused on AI adoption in isolated contexts and few studies have been conducted to understand how consumers perceive these recommendations, especially in different cultural contexts (e.g., individualism vs. collectivism). While previous studies have examined AI's role in changing consumer behaviour and driving sustainability, few studies have been conducted to understand the intersection of AI-driven sustainability efforts and cross-cultural consumer perceptions. This study aims to explore how consumers in the UK and China perceive AI-generated eco-recommendations. Specifically, it focuses on factors such as trust in AI, perceived alignment of recommendations with personal and societal values, and willingness to adopt environmentally friendly behaviours recommended by AI. We adopt a qualitative approach and aim to conduct 15 semi-structured in-depth interviews with consumers in each country to analyse these factors. The findings of this study will offer insights into the role of AI in promoting sustainable consumption behaviour across different cultural contexts. It will also offer insights for policy makers and businesses to tailor their eco-recommendation strategies to specific consumer markets.
Authors: Guo, Xiuli; id_orcid 0000-0003-3751-0170, Zhuang, Caiyong, Khan, Muzammal; id_orcid 0000-0001-9729-5412, Chinnasamy, Jayakumar; id_orcid 0000-0001-7580-7990
Language: English
Topic: AI in Service Interactions
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7038035944
FWCI:
Cites:
Cited by:
Related to:
