Returns Bans and Strategic Management in E-commerce What are the managerial and economic impacts of banning high-frequency returners in online fashion retail : effects on customer relations, market positioning, and business model sustainability ?

(2026)

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Abstract
The fashion e-commerce sector has reached a structural inflection point where the traditional "leniency paradigm" of universal free returns now threatens the core profitability of major retailers. In this industry, return rates have climbed to between 20% and 30%, with approximately 10% of shoppers accounting for nearly 60% of total return-related losses. This thesis investigates the managerial and economic impacts of managing these high-frequency returners and examines the resulting effects on customer relations, market positioning, and business model sustainability. Adopting a sequential mixed-methods design, the research first performs a longitudinal analysis of the 2024-2025 strategic cycles of three industry leaders: ASOS (The Policeman), Zalando (The Engineer), and Inditex (The Integrator). These secondary findings are then subjected to an "Expert Confrontation" phase involving semi-structured interviews with six high-level industry experts to validate the results against field realities. The study introduces a unique X→Y→Z Decision Architecture (Strategic Levers → Measurable Outcomes → Internal Resistances) to formalize the causal relations between specific management actions and financial/environmental performance. The results identify three definitive strategic paths: • The Restrictive Path (ASOS) proves that targeted return fees (e.g., £3.95) can achieve a 30% increase in profit per order and a 19% reduction in Scope 3 emissions by culling unprofitable volume. • The Technical Path (Zalando) demonstrates how an AI-driven "Matchmaking Moat" decoupled an 11% volume surge from fulfillment costs, which remained stable at 23.0%. • The Structural Path (Inditex) establishes store-hub integration as the benchmark for efficiency, protecting a record 58.3% gross margin by outrunning the accelerated obsolescence risk of seasonal merchandise. The empirical robustness of this framework is validated through the triangulation of longitudinal financial data, six expert interviews, and peer-reviewed academic literature. However, the research uncovers critical Internal Strategic Resistances (Z) such as the operational capacity constraints, the “Fitting Paradox” of AI, and the “German Exception” in logistics density that annual reports often minimize. Ultimately, this thesis provides a field-adjusted Managerial Decision Toolkit, transforming returns from a back-office cost center into a front-office strategic moat for the 2026-2030 cycle.