An increasing number of companies engage in the customization of products to reap the benefits of increased sales and the potential to charge higher prices. Online configurators, which allow customers to assemble “customized” versions of a product, play an important role in customization. In particular, they help companies learn about user preferences and generate reliable forecasts. Therefore, menu-based choice experiments have gained increased attention in recent years. Despite their high relevance, little is known about the properties of the different modeling approaches under varying study conditions. We compare four prominent modeling approaches in an extensive simulation study that systematically varies respondent heterogeneity, choice menu complexity, the available sample size, the number of individual tasks, and the underlying behavioral model. We evaluate the models for reservation price recoverability, model fit, and predictive validity. The findings show that in most cases, one obtains better results for simple and straightforward representations of respondent behavior (e.g., separate multinomial logit models for different functional areas) than for the more sophisticated modeling approaches (e.g., probit-based approaches).

越来越多的公司从事产品定制，以从销售增加中获益，并有可能收取更高的价格。允许客户组装产品“定制”版本的在线配置器在定制中起着重要作用。特别是，它们可以帮助公司了解用户的偏好并生成可靠的预测。因此，基于菜单的选择实验近年来受到越来越多的关注。尽管它们具有很高的相关性，但是对于在不同研究条件下不同建模方法的属性知之甚少。我们在广泛的模拟研究中比较了四种著名的建模方法，这些方法系统地改变了受访者的异质性，选择菜单的复杂性，可用样本量，单个任务的数量，以及潜在的行为模型。我们评估模型的预订价格可恢复性，模型拟合和预测有效性。研究结果表明，与更复杂的建模方法（例如，基于概率的方法）相比，在大多数情况下，对于响应者行为的简单直接表示（例如，针对不同功能区域的单独的多项式logit模型），可以获得更好的结果。