Nasreddine El Dehaibi

Designers are challenged to create sustainable products that succeed in the marketplace, often relying on life cycle analyses to identify engineered sustainable features while neglecting perceived-as-sustainable (PAS) features. PAS features may not contribute to engineered sustainability but are identified by customers as sustainable. In previous papers, we proposed methods for extracting PAS features from online reviews using machine learning techniques and validating them using collage placement techniques. We demonstrated our methods using French presses (and other products). In this paper, we combined design and marketing approaches to test previously extracted PAS features in terms of purchasing products that include PAS features, as compared to others that do not. We built a simulated Amazon shopping experience using incentive alignment and constructed a within-subject, fractional factorial design with a variety of product features and physical appearances. We collected data on purchase intent, willingness to pay, and sustainability rating. We found that participants opted to purchase products with PAS features more often than products with features that are not PAS, termed “dummy” features. Participants also indicated they were willing to pay more for products with PAS features and rated those products as more sustainable, despite the features not contributing to engineered sustainability. Our findings demonstrate the potential value of identifying and including PAS features in sustainable products and a new application for shopping simulation experiments in design research. We recommend that sustainable designers include both engineered and PAS features in sustainable products to align with customer needs, drive purchasing decisions, and potentially increase profitability.

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.

Designers are challenged to create sustainable products that resonate with customers, often focusing on engineered sustainability while neglecting perceived sustainability. We previously proposed a method for extracting perceived sustainable features from online reviews using annotations and natural language processing (NLP), testing our method with French press coffee carafes. We identified that perceived sustainability may not always align with engineered sustainability. We now investigate how designers can validate perceived features extracted from online reviews using a relatively new design method of collage placement where participants drag and drop products on a two-by-two axis collage and select features from a dropdown menu. We created collage activities for participants to evaluate French press products on the three aspects of sustainability: social, environmental, and economic, and on how much they like the products. During the activity, participants placed products along the two axes of the collage, sustainability and likeability, and labeled products with descriptive features. We found that participants more often selected our previously extracted features when placing products higher on the sustainability axis, validating that the perceived sustainable features resonate with users. We also measured a low correlation between the two axes of the collage activity, indicating that perceived sustainability and likeability can be measured separately. In addition, we found that product perceptions across sustainability aspects may differ between demographics. Based on these results, we confirm that the collage is an effective tool for validating sustainability perceptions and that features perceived as sustainable from online reviews resonate with customers when thinking of various sustainability aspects.

An important step when designers use machine learning models is annotating user generated content. In this study we investigate inter-rater reliability measures of qualitative annotations for supervised learning. We work with previously annotated product reviews from Amazon where phrases related to sustainability are highlighted. We measure inter-rater reliability of the annotations using four variations of Krippendorff's U-alpha. Based on the results we propose suggestions to designers on measuring reliability of qualitative annotations for machine learning datasets.

In order for a sustainable product to be successful in the market, designers must create products that are not only sustainable in reality but are also sustainable as perceived by the customer—and reality versus perception of sustainability can be quite different. This paper details a design method to identify perceptions of sustainable features (PerSFs) by collecting online reviews, manually annotating them using crowdsourced work, and processing the annotated review fragments with a natural language machine learning algorithm. We analyze all three pillars of sustainability—social, environmental, and economic—for positive and negative perceptions of product features of a French press coffee carafe. For social aspects, the results show that positive PerSFs are associated with intangible features, such as giving the product as a gift, while negative PerSFs are associated with tangible features perceived as unsafe, like sharp corners. For environmental aspects, positive PerSFs are associated with reliable materials like metal while negative PerSFs are associated with the use of plastic. For economic aspects, PerSFs mainly serve as a price constraint for designers to satisfy other customer perceptions. We also show that some crucial sustainability concerns related to environmental aspects, like energy and water consumption, did not have a significant impact on customer sentiment, thus demonstrating the anticipated gap in sustainability perceptions and the realities of sustainable design, as noted in previous literature. From these results, online reviews can enable designers to extract PerSFs for further design study and to create products that resonate with customers' sustainable values.