<![CDATA[The rapid evolution of running shoe technology, particularly within the category of carbon-plated "super shoes," has radically transformed the athletic footwear market. Modern retailers like Super Shoes face a complex Multi-Criteria Decision Making (MCDM) challenge when offering objective, personalized recommendations to customers with diverse athletic profiles and financial constraints. Traditional sales recommendations provided by staff are often highly subjective, intuitive, and prone to sales-target bias; this creates a risk of financial loss for consumers due to poor product choices (sunk costs) and increases the likelihood of musculoskeletal biomechanical injuries among runners. This study proposes a Decision Support System (DSS) framework utilizing the Step-wise Weight Assessment Ratio Analysis (SWARA) method to generate criteria weightings that are structured, dynamic, and mathematically consistent. The model evaluates three elite-tier flagship running shoe variants as alternatives: the Nike Alphafly 3, Adidas Adizero Adios Pro 4, and ASICS Metaspeed Sky Paris. The evaluation process is based on five comprehensive criteria: Price (C1), Cushioning Comfort (C2), Shoe Weight (C3), Outsole Durability (C4), and Energy Return Responsiveness (C5). Computational results demonstrate that the SWARA method effectively minimizes expert cognitive fatigue during the preference extraction process and successfully avoids the consistency error issues associated with pairwise comparison matrices in conventional AHP models. By integrating a simple additive normalization technique, the system successfully ranks alternatives according to the specific priorities of runners. This confirms that a SWARA-based DSS provides an adaptive, efficient, and transparent decision-making tool suitable for implementation in the high-end sports retail business environment.]]>
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