This cross-cultural study (individualist vs. collectivist culture) applies the levels of representation theory to explore the impact of cause familiarity on brand attitudes and the mediating effect of cause-brand fit. The research also examines the moderating role of perceived betrayal in the relationship between cause-brand fit and brand attitude. The data, collected from 455 French and Turkish participants via snowball sampling, reveal a significant influence of cause familiarity on brand attitude. Attitude towards fit in a cause-brand alliance acts as a mediator, while perceived betrayal moderates this relationship, highlighting the positive effects of alignment with a familiar cause on brand attitude and underscoring the crucial importance of fit in such alliances.
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Robotic warehouses have transformed logistics, prioritizing speed and efficiency. However, traditional static priority systems often leave low-priority customers facing excessive delays, raising concerns about fairness. This research, based on Invia, a robotic warehouse company, proposes a dynamic priority allocation model to balance efficiency and fairness. By adjusting order priorities over time, this approach ensures that both high-priority and long-waiting low-priority orders receive timely fulfillment. Through stochastic modeling and simulations, we demonstrate that dynamic prioritization reduces delays compared to static and first-come, first-served (FCFS) models. Case studies in e-commerce and healthcare logistics illustrate the broader impact of fairness in automation. As industries increasingly rely on AI-driven decision-making, the balance between efficiency and equity becomes critical. This research challenges the assumption that robotic warehouses should optimize for speed alone and advocates for a future where fairness plays a central role in automated commerce.
- Recherche
- Logistique et Supply Chain, Transformation Digitale
