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 (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.

过往研究多聚焦于鞋类力学性能及其对跑步生物力学的影响，但目前学界对其如何影响跑者满意度，或是消费者对跑鞋的感知优劣程度仍知之甚少。若能基于鞋类力学性能预测跑者对跑鞋的满意度，将对鞋类企业极具应用价值。本研究的数据来源于两部分：一是包含615组受试者-鞋具配对的满意度评分数据库（评分采集于受试者在跑步机上跑步后），二是针对87名独立受试者、61款不同鞋具的力学测试数据。本研究构建了随机森林（Random Forest）与弹性网逻辑回归（Elastic Net Logistic Regression）模型，以验证鞋类力学性能与受试者特征能否从三个维度预测跑者满意度：7点李克特（Likert）量表评分的满意度等级、3点李克特量表评分的整体满意度，以及鞋具购买意愿（是/否二分类响应）。本研究以80-20的比例将数据划分为训练集与验证集，分别用于模型构建与精度测试。模型精度将与无信息率（no-information rate，即数据集中最大类别的占比）进行对比。实验结果显示，模型无法通过鞋类力学性能预测跑者的满意度等级或整体满意度，但可实现68%至75%的购买意愿预测精度。在两款购买意愿预测模型的变量重要性排名中，足跟与前掌区域的中底Gmax（Midsole Gmax）值均位列前五，表明该指标是影响购买决策的关键因素。足跟与前掌Gmax值的回归系数为负值，说明中底越柔软，鞋具的购买概率越高。未来若在满意度预测模型中加入更多受试者专属参数（如跑步目标或足部比例），或可进一步提升预测精度。