This study compared projective mapping (PM) with check‐all‐that apply (CATA) with 24 consumers to traditional preference mapping with 257 consumers for evaluation of drivers of liking for brewed black coffees. For the PM exercise, black coffee consumers (n = 24) evaluated 11 coffees and placed them on a two‐dimensional plane based on similarities and then selected attributes from a provided list (PMCATA) to describe each coffee. These consumers also scored liking of each coffee. PMCATA and liking evaluations were completed in duplicate. A trained descriptive analysis (DA) panel (n = 8) documented properties of the coffees in quadruplicate and black coffee consumers (n = 257) were recruited for traditional consumer acceptance testing. Landscape segmentation analysis (LSA) was applied to trained panel data and consumer acceptance scores. Data from the PMCATA exercise was analyzed using multiple factor analysis (MFA). Consumer groups from the PMCATA exercise were differentiated by preferences for light or dark roast attributes. Similarly, determination of ideal points by LSA revealed that consumer groups were defined primarily by differences in preference for different roast types and roast‐related attributes. Based on the similarities in regard to product characterization and consumer segmentation, these results demonstrate that PMCATA can be an effective preliminary alternative to traditional methods for profiling and consumer preferences of complex products like coffee.

中文翻译：

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偏好映射与投影映射的比较，用于表征消费者对酿造黑咖啡的感知

这项研究比较了有24个消费者的投影映射（PM）和适用所有检查的（CATA）与有257个消费者的传统偏好映射，以评估喜欢冲泡黑咖啡的驱动因素。在PM练习中，黑咖啡消费者（n = 24）评估了11种咖啡，并根据相似度将它们放在二维平面上，然后从提供的列表（PMCATA）中选择属性来描述每种咖啡。这些消费者还对每种咖啡都表示了喜欢。PMCATA和喜欢的评估一式两份完成。训练有素的描述性分析（DA）面板（n = 8）记录了四次重复和黑咖啡消费者中咖啡的特性（n= 257）被招募用于传统的消费者接受度测试。景观分割分析（LSA）已应用于经过培训的面板数据和消费者接受度评分。使用多因素分析（MFA）对来自PMCATA练习的数据进行了分析。PMCATA演习的消费者群体通过对浅色或深色烘烤属性的偏好来区分。同样，LSA确定理想点后发现，消费者群体主要是由对不同烘烤类型和与烘烤相关的属性的偏好差异来定义的。基于产品特征和消费者细分方面的相似性，这些结果表明PMCATA可以有效替代传统方法（如咖啡）和消费者喜好的传统方法。