Soccer shoe recommendation system based on multitechnology integration for digital transformation

Designing a soccer shoe that fits specific customer’s requirements can improve satisfaction. However, there is no sufficient information to bridge the semantic needs and design characteristics of soccer shoes. Hence, this study is aimed at integrating multiple technologies with semantic customer requirements, shoe-form categories, and appearance designs, and developing a practical Kansei soccer shoe recommendation system. The psychological responses of a customer and perceptions of soccer shoe products are evaluated using Kansei engineering method. Based on a factor analysis, customers’ requirements are classified as aesthetic, functional, and comfortable. A total of 203 soccer shoe images were used to evaluate and categorise the external shoe form into nine design elements using the Kawakida Jirou method, and the weight assigned to each shoe-form category under Kansei semantic adjectives was determined using grey system theory. The quantification theory Type 1 method was used to determine the priority of the design elements. The results indicate that 10 pairs of semantic adjectives used by customers are related to soccer shoe forms. Furthermore, the design priority of each design element of the form category under the three types is reported. A soccer shoe recommendation system that can generate suggested soccer shoe samples for customers is developed by integrating the above-mentioned technologies. A validation experiment was conducted to verify the feasibility of the proposed system. The overall satisfaction of the recommended samples generated via the system is 87.08%, as reported by 80 participants. The findings prove that a new soccer shoe business model can be launched using the proposed system, linking the semantic customer requirements to the soccer shoe-form categories.