Firms are developing AI-enhanced products (e.g., robots) that can tackle environmental problems through autonomous interactions with their surroundings (e.g., removing waste/pollutants, tracking invasive species) and autonomous learning, which results in improved environmental performance characteristics. Such autonomous environmental benefits of products differ from conventional, static environmental benefits, which derive from pre-purchase processes and design decisions. However, the literature still lacks knowledge of how to use such autonomous environmental benefits to attract new customers. Therefore, drawing on signaling theory, this study examines the effect of these environmental benefits on a consumer's purchase intent and its variation across types of consumers, locations, and products. Based on hierarchical linear modeling of 1635 consumer evaluations of AI-enhanced products, this study finds that both static and autonomous perceived environmental benefits influence purchase intent positively. The effect of autonomous environmental benefits is stronger for women than for men and for products targeted at adults rather than children. The effect of static environmental benefits is stronger for men than women, for products targeted at children rather than adults, for consumers with a higher need for cognition, and in locations with a higher perceived environmental well-being.
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Strategy and Management
- Industrial and Manufacturing Engineering