Using visual and linguistic framing to support sustainable decisions in an online store

Companies face several digital communication challenges when it comes to promoting green products or services. The framing effect, which refers to the presentation of information, can significantly influence decision-making in digital interfaces. This research explores the impact of information framing through text and visuals on purchase decisions for sustainable fashion products. An online evaluation study (𝑁 = 84) of an e-commerce environment was conducted. We found that visual framing significantly affected user product choices, supporting more sustainability decisions. In contrast, little evidence was found that supported the effectiveness of linguistic (i.e., message) framing on user product choices. We discuss implications on how product pages should be designed to encourage sustainable decision-making

Using visual and linguistic framing to support sustainable decisions in an online store

Companies face several digital communication challenges when it comes to promoting green products or services. The framing effect, which refers to the presentation of information, can significantly influence decision-making in digital interfaces. This research explores the impact of information framing through text and visuals on purchase decisions for sustainable fashion products. An online evaluation study (𝑁 = 84) of an e-commerce environment was conducted. We found that visual framing significantly affected user product choices, supporting more sustainability decisions. In contrast, little evidence was found that supported the effectiveness of linguistic (i.e., message) framing on user product choices. We discuss implications on how product pages should be designed to encourage sustainable decision-making

The interplay between food knowledge, nudges, and preference elicitation methods determines the evaluation of a recipe recommender system

Domain knowledge can affect how a user evaluates different aspects of a recommender system. Recipe recommendations might be difficult to understand, as some health aspects are implicit. The appropriateness of a recommender’s preference elicitation (PE) method, whether users rate individual items or item attributes, may depend on the user’s knowledge level. We present an online recipe recommender experiment. Users (𝑁=360) with varying levels of subjective food knowledge faced different cognitive digital nudges (i.e., food labels) and PE methods. In a 3 (recipes annotated with no labels, Multiple Traffic Light (MTL) labels, or full nutrition labels) x2 (PE method : content-based PE or knowledge-based) between-subjects design. We observed a main effect of knowledge-based PE on the healthiness of chosen recipes, while MTL label only helped marginally. A Structural Equation Model analysis revealed that the interplay between user knowledge and the PE method reduced the perceived effort of using the system and in turn, affected choice difficulty and satisfaction. Moreover, the evaluation of health labels depends on a user’s level of food knowledge. Our findings emphasize the importance of user characteristics in the evaluation of food recommenders and the merit of interface and inter action aspects

Promoting Healthy Food Choices Online: A Case for Multi-List Recommender Systems

Many food choices are made online. Interactive, personalized interfaces, such as recommender systems, can help users to f!nd new products to eat or recipes to cook, but they tend to promote unhealthy alternatives. In this position paper, we argue that better algorithms are not the only way forward. We blend algorithms and user interface design to present a multi-list recommender interface that presents multiple lists of personalized items in a single interface, where each list is optimized for a speci!c feature (e.g., ‘less fat’). We argue how multi-list recommenders can be used to support healthier food choices

Healthiness and environmental impact of dinner recipes vary widely across developed countries

Contrary to food ingredients, little is known about recipes’ healthiness or environmental impact. Here we examine 600 dinner recipes from Norway, the UK and the USA retrieved from cookbooks and the Internet. Recipe healthiness was assessed by adherence to dietary guidelines and aggregate health indicators based on front-of-pack nutrient labels, while environmental impact was assessed through greenhouse gas emissions and land use. Our results reveal that recipe healthiness strongly depends on the healthiness indicator used, with more than 70% of the recipes being classified as healthy for at least one front-of-pack label, but less than 1% comply with all dietary guidelines. All healthiness indicators correlated positively with each other and negatively with environmental impact. Recipes from the USA, found to use more red meat, have a higher environmental impact than those from Norway and the UK

Topical preference trumps other features in news recommendation: A conjoint analysis on a representative sample from Norway

A variety of news articles features can be used to tailor news content. However, only a few studies have actually compared the relative importance of different features in predicting news reading behavior in the context of news recommender systems. This study reports the results of a conjoint experiment, where we examined the relative importance of seven features in predicting a user’s intention to read, including: topic headline (Abortion vs Meat Eating), reading time, recency, geographic distance, topical preference match, demographic similarity, and general popularity in a news recommender system. To ensure an externally valid result, the study was distributed among a representative Norwegian sample (𝑁 = 1664), where users had to choose their preferred news article profile from four different pairs. We found that a topical preference match was by far the strongest predictor for choosing a news article, while recency and demographic similarity had no impact

Effective user interface designs to increase energy-efficient behavior in a Rasch-based energy recommender system

People often struggle to find appropriate energy-saving measures to take in the household. Although recommender studies show that tailoring a system's interaction method to the domain knowledge of the user can increase energy savings, they did not actually tailor the conservation advice itself. We present two large user studies in which we support users to make an energy-efficient behavioral change by presenting tailored energy-saving advice. Both systems use a one-dimensional, ordinal Rasch scale, which orders 79 energy-saving measures on their behavioral difficulty and link this to a user's energy-saving ability for tailored advice. We established that recommending Rasch-based advice can reduce a user's effort, increase system support and, in turn, increase choice satisfaction and lead to the adoption of more energy-saving measures. Moreover, follow-up surveys administered four weeks later point out that tailoring advice on its feasibility can support behavioral change