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Designing Informative Rating Systems: Evidence from an Online Labor Market

机译:设计信息评级系统:来自在线劳动力市场的证据

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Problem definition: Platforms critically rely on rating systems to learn the quality of market participants. In practice, however, ratings are often highly inflated and therefore, not very informative. In this paper, we first investigate whether the platform can obtain less inflated, more informative ratings by altering the meaning and relative importance of the levels in the rating system. Second, we seek a principled approach for the platform to make these choices in the design of the rating system. Academic/practical relevance: Platforms critically rely on rating systems to learn the quality of market participants, and so, ensuring these ratings are informative is of first-order importance. Methodology: We analyze the results of a randomized, controlled trial on an online labor market in which an additional question was added to the feedback form. Between treatment conditions, we vary the question phrasing and answer choices; in particular, the treatment conditions include several positive-skewed verbal rating scales with descriptive phrases or adjectives providing specific interpretation for each rating level. We then develop a model-based framework to compare and select among rating system designs and apply this framework to the data obtained from the online labor market test. Results: Our test reveals that current inflationary norms can be countered by reanchoring the meaning of the levels of the rating system. In particular, positive-skewed verbal rating scales yield substantially deflated rating distributions that are much more informative about seller quality. Further, we demonstrate that our model-based framework for scale design and optimization can identify the most informative rating system and substantially improve the quality of information obtained over baseline designs. Managerial implications: Our study illustrates that practical, informative rating systems can be designed and demonstrates how to compare and design them in a principled manner.
机译:问题定义:平台批评依赖评级系统来学习市场参与者的质量。然而,在实践中,评级往往是高度膨胀,因此不是很好的信息。在本文中,我们首先调查平台是否可以通过改变评级系统中水平的含义和相对重要性来获得更少的膨胀,更有信息的评级。其次,我们为平台寻求原则性的方法,使这些选择在评级系统的设计中。学术/实际相关性:平台依赖评级系统来学习市场参与者的质量,因此,确保这些评级是信息性的,这是一流的重要性。方法论:我们分析了在线劳动力市场的随机,对照试验的结果,其中将额外的问题添加到反馈表中。在治疗条件之间,我们改变了问题措辞和答案选择;特别地,治疗条件包括几个正偏斜的口头评级尺度,具有描述性短语或形容词,为每个评级水平提供特定的解释。然后,我们开发基于模型的框架来比较和选择评级系统设计,并将此框架应用于从在线劳动力市场测试中获取的数据。结果:我们的测试表明,通过加速系统的含义,可以通过加速系统的含义来抵消当前的通胀规范。特别是,正偏斜的言语评级尺度产生了大量放气的评级分布,这些分布是卖家质量的更具信息量。此外,我们证明了我们的模型设计和优化的模型框架可以识别最具信息丰富的评级系统,并大大提高了通过基线设计获得的信息的质量。管理含义:我们的研究说明了实用的,信息丰富的评级系统可以设计和演示如何以原则方式比较和设计它们。

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