Laurette Dubé

Consumers often decide what to share by estimating how much others will value a piece of content. We show that these estimates are systematically miscalibrated for emotionally valenced material. Across four studies spanning controlled tasks and naturalistic contexts, people overestimated others’ valuation of valenced, especially negative, content relative to others’ self‐reports and willingness to pay. Process evidence indicates that valence makes highly affected exemplars salient; focusing on these recipients inflates beliefs about audiences’ valuations, which in turn predicts higher sharing intentions. A lightweight focus‐switching prompt that asks senders to consider a specific close friend reduces this bias to narrow the valenced–neutral sharing gap. These findings recast the spread of valenced content as a judgment bias about what audience value, not reducible to other established drivers of sharing, integrating value‐based sharing with projection biases in consumer judgment. Managerially, interface prompts and audience‐specification defaults can rebalance sharing toward content that audiences actually value, with implications for engagement quality, customer decisions, and consumer welfare.

Commercial markets are institutional environments that shape which products are available and how value is signalled to and perceived by consumers. This study examines how such environments shape Indigenous participation in market exchanges, using Traditional Foods (TFs) in grocery retail as an empirical case. Using loyalty card data from 244 grocery stores in Quebec (2015–2016), we first assessed TF availability and found that only 34.5% of reference TF items appeared in retail assortments, with representation concentrated in categories compatible with conventional supply chains and no products supplied by Indigenous enterprises. We then estimated TF demand using two‐level Tobit models, comparing First Nations and general population consumers. Results revealed no differences in baseline price sensitivity, but stronger responsiveness among First Nations consumers to local, organic and fresh product attributes, as well as promotional cues. Together, the findings revealed inequalities in Indigenous market participation reflected through selective availability and the interpretation of standardized market signals rather than differential price responsiveness. Results are discussed in terms of the possibility spaces opened for Indigenous entrepreneurs on both supply/demand sides of the food value chain to build bridges between local and industrial markets and partake in economic reconciliation at scale. Merchandizing and marketing pathways in this direction are suggested.

Shifting demand toward plant-based proteins is an important sustainability transition, yet adoption remains limited. Here, we used longitudinal loyalty data from 29,131 Finnish consumers to estimate differences in price elasticity between plant- and animal-based proteins in Study 1. Study 2a examined socioeconomic status as a moderator of price elasticity, while Studies 2b and 2c assessed income and education separately. Study 3 compared estimates when measured at the individual versus the neighborhood level. A robustness check replicated analyses in Canada. Two-level mixed-effects regression models revealed that consumers were less price elastic to plant proteins than animal proteins. Socioeconomic status moderated both, but disparities were over three times larger for animal proteins. Income moderated price elasticity, while education’s effect was stronger for animal proteins. Neighborhood-level measures yielded similar patterns. Results were comparable in Canada. Differences in price sensitivity by socioeconomic status are central to the protein transition and should be incorporated into future sustainability interventions.

Sustainable diets are increasingly promoted to reduce environmental impacts while supporting public health. However, making sustainable and healthy food choices remains challenging due to the complex interplay of individual behaviors, preferences, and environmental influences. This study evaluated whether a digital food environment—the Dashboard for Improving Sustainable Healthy (DISH) food choices—could encourage more environmentally sustainable and nutritionally balanced meal selections by presenting real-time information in an engaging format. The DISH platform was deployed in three media formats (mobile app, website, kiosk) in collaboration with two university campus cafeterias over a 10-week period. Behavioral outcomes were measured using pre- and post-intervention nutritional scores (Food Compass Score, Health Star Rating) and environmental impact indicators (climate change score). Data were collected from 189 participants (85 controls, 86 interventions), supplemented by surveys and qualitative interviews with 26 intervention participants four months later, guided by the COM-B model. Non-parametric analyses (Wilcoxon and Mann-Whitney U tests) indicated that exposure to DISH’s environmental-nutrition information did not significantly influence food choices on any behavioral measure (p > 0.47). A latent profile analysis identified a subgroup characterized by high health motivation but low climate consideration, suggesting a promising target for future interventions, such as tailored messaging or strategic nudges aligned with both health and environmental goals. Qualitative feedback revealed that participants viewed DISH as a useful tool for increasing awareness and reinforcing sustainability concepts. Overall, the study highlights both the challenges and opportunities associated with using digital tools like DISH to enhance sustainability awareness and support informed dietary decisions. Recommendations for further development focus on overcoming barriers to behavioral impact and enhancing the application’s motivational and contextual relevance.

Achieving food system transformation requires a deep understanding of the market mechanisms that underpin both the social benefits and the externalities of modern development. We examine how market dynamics affect the production and consumption of healthy and equitable diets in North America. Using causal loop diagramming, we show how three market feedback processes (industry capabilities, consumer category considerations, and systems and institutions) both constrain and enable food system transformation. Through behavioral-dynamic computational modeling, we demonstrate the ineffectiveness of isolated social or commercial interventions to achieve equitable access to nutritious foods across populations of varying socioeconomic statuses. Rather, self-sustaining transformations at scale require convergent innovations that bridge individual and collective action across typically siloed sectors, to achieve alignment between commercial, social, and environmental goals and activities. We discuss how this simulation-based analytical framework can inform policy for food system transformation, whether at the local, national, or global level.

This study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering algorithms, we classify 295,631 loyalty program members into seven distinct consumer segments based on behavioural and sociodemographic attributes. The findings reveal significant heterogeneity in consumer behaviour, emphasizing the impact of urban geography on shopping decisions. This segmentation also provides valuable insights for retailers optimizing store locations and marketing strategies and for policymakers aiming to enhance urban accessibility. Additionally, our approach strengthens agent-based model (ABM) simulations by incorporating demographic and behavioural diversity, leading to more realistic consumer representations. While integrating loyalty data with synthetic populations mitigates privacy concerns, challenges remain regarding data sparsity and demographic inconsistencies. Future research should explore multi-source data integration and advanced clustering methods. Overall, this study contributes to geographically explicit modelling, demonstrating the effectiveness of combining behavioural and synthetic demographic data in urban retail analysis.

This study integrates customer loyalty program data with a synthetic population to analyze grocery shopping behaviours in Montreal. Using clustering techniques, we classify 295,631 loyalty program members into seven distinct consumer segments based on behavioural and sociodemographic attributes. The findings reveal significant heterogeneity in consumer behaviour, emphasizing the impact of urban geography on shopping decisions. This segmentation also provides valuable insights for retailers optimizing store locations and marketing strategies, and for policymakers aiming to enhance urban accessibility. Additionally, our approach strengthens Agent-Based Model (ABM) simulations by incorporating demographic and behavioural diversity, leading to more realistic consumer representations. While integrating loyalty data with synthetic populations mitigates privacy concerns, challenges remain regarding data sparsity and demographic inconsistencies. Future research should explore multi-source data integration and advanced clustering techniques. Overall, this study contributes to geographically explicit modelling, demonstrating the effectiveness of combining behavioural and synthetic demographic data in urban retail analysis.