Can footwear satisfaction be predicted from mechanical properties?

• Published in: Footwear science Vol. 14; no. 3; pp. 151 - 161
• Main Authors: Salzano, Matthew Q., Weir, Gillian, Thompson, Jessica, Trudeau, Matthieu B., Ertel, Christopher, Dear, Kayley, Willwacher, Steffen, Hamill, Joseph
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.09.2022; Taylor & Francis Ltd
• Subjects: Accuracy; Biomechanics; Decision trees; Footwear; Footwear mechanical properties; footwear satisfaction; Heels; Likert scale; logistic regression; Mechanical properties; Mechanical tests; Model accuracy; predictive model; random forest; Regression coefficients; Regression models; Running; Shoes; Treadmills; Uniqueness
• ISSN: 1942-4280; 1942-4299
• DOI: 10.1080/19424280.2022.2077843

Research is often conducted to investigate footwear mechanical properties and their effects on running biomechanics, but little is known about their influence on runner satisfaction, or how well the shoe is perceived. A tool to predict runner satisfaction in a shoe from its mechanical properties would be advantageous for footwear companies. Data in this study were from a database (n = 615 subject-shoe pairings) of satisfaction ratings (gathered after participants ran on a treadmill), and mechanical testing data for 87 unique subjects across 61 unique shoes. Random forest and elastic net logistic regression models were built to test if footwear mechanical properties and subject characteristics could predict runner satisfaction in 3 ways: degree-of-satisfaction on a 7-point Likert scale, overall satisfaction on a 3-point Likert scale, and willingness-to-purchase the shoe (yes/no response). Data were divided into training and validation sets, using an 80-20 split, to build the models and test their accuracy, respectively. Model accuracies were compared against the no-information rate (i.e. proportion of data belonging to the largest class). The models were not able to predict degree-of-satisfaction or overall satisfaction from footwear mechanical properties but could predict runner's willingness to purchase with 68-75% accuracy. Midsole Gmax at the heel and forefoot appeared in the top five of variable importance rankings across both willingness-to-purchase models, suggesting its role as a major factor in purchase decisions. The negative regression coefficient for both heel and forefoot Gmax indicated that softer midsoles increase the likelihood of a shoe purchase. Future models to predict satisfaction may improve accuracy with the addition of more subject-specific parameters, such as running goals or foot proportions.