Measuring Product Type With Dynamics of Online Product Review Variance
Measuring Product Type With Dynamics of Online Product Review Variance
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发表时间：
2012-08
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通讯作者：
Y. Hong;Pei-yu Chen;L. Hitt
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作者：
Y. Hong;Pei-yu Chen;L. Hitt
“产品类型”的概念（体验与搜索产品）在商业研究和实践中变得越来越重要。然而，在互联网时代，由于搜索成本显着降低以及对信息和通信技术的依赖导致消费者信息搜索行为发生变化，它并没有得到精确的定义或衡量。然而，正确理解产品类型对于营销策略和数字市场设计具有重要的战略意义。我们利用大量可用的微观在线口碑数据，并根据在线口碑（特别是在线产品评论）的统计特性推断产品类型（体验与搜索产品）。我们利用大数定律（L.L.N）以及有关信息内容和在线产品评论的文献，分析性地提出了一种产品分类机制。我们的理论分析表明，对于纯搜索产品，当评论数量（即评论样本量）随着更多消费者对产品进行评分而增加时，平均评分的方差将会减小。而对于具有更多体验属性的产品，当评论数量增加时，平均评分的方差不会减少，反而可能会增加，具体取决于这些体验属性的主导程度。我们从三个不同的网站（Amazon、Yelp 和 Ctrip）收集档案数据，这些网站收集和发布消费者产品评论，以对产品和服务进行分类。讨论了这种分析工具和实证研究结果对研究、理论和管理实践的影响。
The concept of “product type” (experience versus search product) is increasingly important in business research and practice. However, it is not defined or measured precisely in the Internet age due to significantly lower search cost and changes in consumer information search behavior resulting from reliance on information and communications technology.. However, correctly understanding product type has important strategic implications for marketing strategy and digital market design. We take advantage of the greatly available micro level online word-of-mouth data and infer product type (experience versus search product) based on statistical properties of online word of mouth (specifically, online product reviews). We draw on the law of large numbers (L.L.N), and the literature on informational content and online product reviews to analytically propose a mechanism to classify products. Our theoretical analyses indicate that, for a pure search product, when number of reviews (i.e. review sample size) increases as more consumers rate the product, variance of the mean rating will decrease. And for a product with more experience attributes, when number of reviews increases, the variance of the mean rating will not decrease and may instead increase depending on how dominant these experience attributes are. We collect archival data from three different websites (Amazon, Yelp and Ctrip) that collect and publish consumer product reviews to categorize the products and services. Implications of this analytical tool and empirical findings for research, theory and managerial practice are discussed.