Integrative Ethical Algorithmic Monitoring in Food Delivery: A Framework for Enhanced Managerial Decision-Making

Vollständiger Abstract

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The rapid expansion of food delivery platforms, governed by opaque algorithmic systems, presents a critical managerial challenge: balancing relentless operational efficiency with fundamental ethical imperatives like fairness, transparency, and accountability. To address this, this study designs and evaluates a novel Integrative Ethical Algorithmic Monitoring (IEAM) framework aimed at enhancing the quality of managerial decision-making. Employing a rigorous design science research approach, our methodology integrates conceptual synthesis from algorithmic management and ethical AI literatures with scenario-based simulations. These simulations utilize structured dummy data modeling a mid-sized platform's operations 50,000 orders and 500 drivers across diverse zones to test the framework’s impact on key decision scenarios such as surge allocation and rating disputes. Key results demonstrate that the IEAM framework, which operationalizes ethics into dashboard metrics, significantly improves ethical outcomes. It reduced simulated order assignment disparity from 20% to 7% and increased the justified overturn rate for disputed algorithmic penalties by 40%. While marginal trade-offs in delivery time and processing costs were observed, the framework consistently shifted decisions toward greater equity. Its primary contribution is a validated, pragmatic tool that bridges abstract ethical AI principles with the daily realities of platform management. This enables more legitimate, sustainable, and data-driven governance, offering managers a proactive mechanism for responsible algorithmic oversight.