In just-about-right (JAR) scaling and ideal scaling, attribute delta (i.e., "Too Little" or "Too Much") reflects a subject's dissatisfaction level for an attribute relative to their hypothetical ideal. Dissatisfaction (attribute delta) is a different construct from consumer acceptability, operationalized as liking. Therefore, we hypothesized minimizing dissatisfaction and maximizing liking would yield different optimal formulations. The objective of this research was to compare product optimization strategies, i.e. maximizing liking vis-à-vis minimizing dissatisfaction. Coffee-flavored dairy beverages (n = 20) were formulated using a fractional mixture design that constrained the proportions of coffee extract, milk, sucrose, and water. Participants (n = 388) were randomly assigned to one of three research conditions, where they evaluated 4 of the 20 samples using an incomplete block design. Samples were rated for overall liking and for intensity of the attributes sweetness, milk flavor, thickness and coffee flavor. Where appropriate, measures of overall product quality (Ideal_Delta and JAR_Delta) were calculated as the sum of the absolute values of the four attribute deltas. Optimal formulations were estimated by: a) maximizing liking; b) minimizing Ideal_Delta; or c) minimizing JAR_Delta. A validation study was conducted to evaluate product optimization models. Participants indicated a preference for a coffee-flavored dairy beverage with more coffee extract and less milk and sucrose in the dissatisfaction model compared to the formula obtained by maximizing liking. That is, when liking was optimized, participants generally liked a weaker, milkier and sweeter coffee-flavored dairy beverage. Predicted liking scores were validated in a subsequent experiment, and the optimal product formulated to maximize liking was significantly preferred to that formulated to minimize dissatisfaction by a paired preference test. These findings are consistent with the view that JAR and ideal scaling methods both suffer from attitudinal biases that are not present when liking is assessed. That is, consumers sincerely believe they want 'dark, rich, hearty' coffee when they do not. This paper also demonstrates the utility and efficiency of a lean experimental approach.

中文翻译：

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最大限度地提高整体喜好会产生优质的产品，最大限度地减少与理想评级的偏差：咖啡味牛奶的优化案例研究

在恰到好处 (JAR) 标度和理想标度中，属性增量（即，“太少”或“太多”）反映了受试者相对于他们的假设理想对属性的不满程度。不满意（属性增量）是与消费者可接受性不同的构造，可操作为喜欢。因此，我们假设最小化不满和最大化喜好会产生不同的最佳公式。本研究的目的是比较产品优化策略，即最大化喜好与最小化不满意。咖啡风味的乳制品饮料（n = 20）使用限制咖啡提取物、牛奶、蔗糖和水比例的分数混合设计配制。参与者（n = 388）被随机分配到三个研究条件之一，他们使用不完整的区组设计评估了 20 个样本中的 4 个。对样品的总体喜好和甜度、牛奶风味、浓稠度和咖啡风味等属性的强度进行评级。在适当的情况下，整体产品质量（Ideal_Delta 和 JAR_Delta）的度量计算为四个属性增量的绝对值之和。通过以下方式估计最佳配方： a) 最大化喜好；b) 最小化 Ideal_Delta；或 c) 最小化 JAR_Delta。进行了验证研究以评估产品优化模型。与通过最大化喜好获得的配方相比，参与者表示在不满意模型中更喜欢咖啡提取物更多、牛奶和蔗糖更少的咖啡味乳制品饮料。也就是说，当喜好被优化时，参与者普遍喜欢较弱的，奶味更浓、更甜的咖啡味乳饮料。预测的喜好分数在随后的实验中得到验证，通过配对偏好测试​​，为最大限度地增加喜好而配制的最佳产品明显优于为最大限度地减少不满意而配制的产品。这些发现与 JAR 和理想缩放方法都存在态度偏差的观点一致，这种偏差在评估喜好时并不存在。也就是说，消费者真诚地相信他们想要“深色、浓郁、浓郁”的咖啡，但实际上他们并不想要。本文还展示了精益实验方法的实用性和效率。通过配对偏好测试​​，为最大限度地增加喜好而配制的最佳产品明显优于为最大限度地减少不满意而配制的产品。这些发现与 JAR 和理想缩放方法都存在态度偏差的观点一致，这种偏差在评估喜好时并不存在。也就是说，消费者真诚地相信他们想要“深色、浓郁、浓郁”的咖啡，但实际上他们并不想要。本文还展示了精益实验方法的实用性和效率。通过配对偏好测试​​，为最大限度地增加喜好而配制的最佳产品明显优于为最大限度地减少不满意而配制的产品。这些发现与 JAR 和理想缩放方法都存在态度偏差的观点一致，这种偏差在评估喜好时并不存在。也就是说，消费者真诚地相信他们想要“深色、浓郁、浓郁”的咖啡，但实际上他们并不想要。本文还展示了精益实验方法的实用性和效率。