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[英文文献] Is Presentation Everything? Using Visual Presentation of Attributes in Disc... [推广有奖]

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韦伯分布602 发表于 2006-2-18 14:59:15 |AI写论文

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英文文献:Is Presentation Everything? Using Visual Presentation of Attributes in Discrete Choice Experiments to Measure the Relative Importance of Intrinsic and Extrinsic Beef Attributes-是表示一切吗?利用离散选择实验中属性的视觉呈现来测量牛肉内在属性和外在属性的相对重要性
英文文献作者:Umberger, Wendy J.,Mueller, Simone C.
英文文献摘要:
A unique discrete choice experiment (DCE) is used to estimate the relative importance of quality attributes to Australian beef consumers. In the DCE, consumers choose their preferred beef steaks from options varying in a large number of intrinsic (marbling and fat trim) and extrinsic/credence (brand, health, forage, meat standards/quality, and production and process claims) attributes. This study is the only known DCE to present these attributes to consumers visually – in a manner that more realistically simulates the retail choice scenario for beef and allows us to evaluate the relative importance of attributes that consumers use both consciously and unconsciously when making product choices. Respondents’ beef choices were analyzed using a latent class scale adjusted choice model. We address two import issues that have potentially strong implications for the validity of estimated attribute values: intrinsic attributes are likely to be underestimated in their importance if not presented visually; and DCEs that exclude important attributes (such as intrinsic characteristics) are likely to overestimate the value of product characteristics. The results suggest that visual attribute level presentation in DCEs results in less biased value estimates. Therefore, it is not only important to consider what attributes to include, but also how you present the attributes.

一个独特的离散选择实验(DCE)被用来估计质量属性对澳大利亚牛肉消费者的相对重要性。在DCE中,消费者从大量内在(大理石纹和脂肪修剪)和外在/信誉(品牌、健康、饲料、肉类标准/质量、生产和加工要求)属性的不同选项中选择他们喜欢的牛排。这项研究是唯一已知的将这些属性可视化地呈现给消费者的DCE——以一种更真实地模拟牛肉零售选择场景的方式,使我们能够评估消费者在做出产品选择时有意或无意使用的属性的相对重要性。使用潜在等级规模调整选择模型分析受访者的牛肉选择。我们解决了两个重要问题,它们对估计属性值的有效性有潜在的强烈影响:如果没有可视化地呈现,内在属性的重要性很可能被低估;而排除了重要属性(如内在特征)的DCEs很可能会高估产品特征的价值。结果表明,在DCEs中可视化的属性水平表示可以减少偏差值估计。因此,不仅要考虑要包含哪些属性,而且要考虑如何表示这些属性。
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