This cross-cultural study (individualistic vs. collectivistic culture) applies construal level theory, exploring the impact of cause familiarity on brand attitudes and how cause–brand fit mediates this link. The study also examines how perceived betrayal moderates the relationship between cause–brand fit and brand attitude. Data collection involved 455 participants from French and Turkish cultures via snowball sampling. Findings show cause familiarity significantly influences brand attitude, with attitude toward fit in a cause–brand alliance as a mediator. Perceived betrayal also moderates the cause–brand fit and brand attitude relationship, shedding light on the positive effects of aligning with a familiar cause on brand attitudes, emphasizing the crucial role 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.
- Research
- Digital Transformation, Logistics and Supply Chain