Can footwear satisfaction be predicted from mechanical properties?

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 (<i>n</i> = 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.

现有研究多围绕鞋类力学性能及其对跑步生物力学的影响展开，但目前针对鞋类性能如何影响跑步者满意度，或是跑鞋的感知优劣程度，相关研究仍较为匮乏。若能基于跑鞋力学性能预测跑步者满意度，将对鞋类企业极具应用价值。本研究数据来源于一个涵盖两类信息的数据库：其一为615组受试者-鞋具配对的满意度评分数据（采集于受试者在跑步机上完成跑步后），其二为针对87名独立受试者与61款不同跑鞋的力学性能测试数据。本研究构建了随机森林（Random Forest）与弹性网络逻辑回归（Elastic Net Logistic Regression）模型，从三个维度验证鞋类力学性能与受试者特征能否预测跑步者满意度：分别为基于7级李克特（Likert）量表的满意度程度评分、基于3级李克特量表的整体满意度评价，以及购买该鞋的意愿（是/否二分类响应）。数据以80:20的比例划分为训练集与验证集，分别用于模型构建与精度测试。将模型的预测精度与无信息率（即数据集中最大类别所占的样本比例）进行对比。实验结果显示，上述模型无法通过鞋类力学性能预测跑步者的满意度程度或整体满意度，但可实现68%~75%的准确率预测跑步者的购买意愿。在两款购买意愿预测模型的变量重要性排名前五的特征中，足跟与前掌处的中底Gmax（Midsole Gmax）均位列其中，表明该指标是影响购买决策的关键因素。足跟与前掌Gmax对应的回归系数为负值，说明中底越柔软，跑鞋被购买的概率越高。未来若在满意度预测模型中加入更多受试者特异性参数（如跑步目标或足部比例），或可进一步提升模型的预测精度。