Extracting Summary Piles from Sorting Task Data
提出一种灵活的分析框架和优化方法，从消费者分类任务数据中提取摘要堆，帮助研究者快速探索项目间的关联，并通过模拟和实证验证其可扩展性和效率。
In a sorting task, consumers receive a set of representational items (e.g., products, brands) and sort them into piles such that the items in each pile “go together.” The sorting task is flexible in accommodating different instructions and has been used for decades in exploratory marketing research in brand positioning and categorization. However, no general analytic procedures yet exist for analyzing sorting task data without performing arbitrary transformations to the data that influence the results and insights obtained. This manuscript introduces a flexible framework for analyzing sorting task data, as well as a new optimization approach to identify summary piles, which provide an easy way to explore associations consumers make among a set of items. Using two Monte Carlo simulations and an empirical application of single-serving snacks from a local retailer, the authors demonstrate that the resulting procedure is scalable, can provide additional insights beyond those offered by existing procedures, and requires mere minutes of computational time.