Differentiating online products using customer perceptions of sustainability

Abstract Customers make quick judgments when shopping online based on how they perceive product design features. These features can be visual such as material or can be descriptive like a ‘nice gift’. Relying on feature perceptions can save customers time but can also mislead them to make uninformed purchase decisions, for example, related to sustainability. In a previous study, we developed a method to extract product design features perceived as sustainable from Amazon reviews, identifying that customer perceptions of product sustainability may differ from engineered sustainability. We previously crowdsourced annotations of French press reviews and used a natural language processing algorithm to extract the features. While these features may not contribute to engineered sustainability, customers identify the features as sustainable enabling them to make informed purchase decisions. In this study, we validate how our previously developed method can be generalised by testing it with electric scooters and baby glass bottles. Features perceived as sustainable for both products are extracted and second, participants are tested on interpreting the features using a novel collage approach. Participants placed products on a set of two axes and selected features from a list. Our results confirm that the proposed method is effective for identifying features perceived as sustainable, and that it can generalise for different products with limitations. Positively biased Amazon reviews can limit the natural language processing performance. We recommend that designers use our method when designing products to capture feature perceptions and help inform customer-oriented design decisions.