Exploring Digital Social Norms Nudges in E-Grocery: Typical Consumer Testimonials with a Warm Glow

Digitization offers several possibilities to alter consumer decisions to support social concerns. The objective of this study is to examine the impact of personalized digital social norm nudges on consumer decisions enriched with the theory of warm glow on e-grocery buying decisions with the aim of supporting social projects. Specific pro-social behaviors targeted were supporting fair payment of the producers of grocery goods, social inclusion projects and initiatives against poverty by deciding for a specific choice option. A between-subjects experiment was performed with the help of a questionnaire using a mock-up mobile grocery store to measure product choices. Results showed that claims supporting pro-social initiatives have a significant impact on buying decisions. Perceived product recommendation influenced our model positively, while we had a negative price impact. The study suggests that warm glow theory and enriched social norm nudges are effective tools for behavior change towards social initiatives

Trade-offs and synergies of digital choice environments: Towards a taxonomy and configurational model

The advancing digitalization of daily life has led to an increasing number of choices in the digital sphere. User interfaces that require either a judgment or a decision, the so-called digital choice environments (DCEs), are essential focal points for interventions to alter behaviors towards individual or societal welfare. However, there is a lack of descriptive and prescriptive knowledge within the field of DCEs. In this research, we follow a multi-stage approach to classify the characteristics of DCEs from a choice-centric viewpoint and disclose configurational trade-offs. To achieve this, we first build a taxonomy of DCEs that we validate through expert interviews. Subsequently, we use cluster analysis to identify four configurations of DCEs, which serve as the basis for the development of a configurational model that outlines configuration-specific user outcomes. Our results contribute to the existing knowledge of digital value creation as well as the explanatory understanding of trade-offs among different DCEs