Designing Informative Rating Systems: Evidence from an Online Labor Market

评定量表 质量(理念) 评级制度 计算机科学 意义(存在) 相关性(法律) 考试(生物学) 订单(交换) 人工智能 心理学 经济 古生物学 哲学 发展心理学 环境经济学 法学 心理治疗师 认识论 生物 政治学 财务
作者
Nikhil Garg,Ramesh Johari
出处
期刊:Manufacturing & Service Operations Management [Institute for Operations Research and the Management Sciences]
卷期号:23 (3): 589-605 被引量:21
标识
DOI:10.1287/msom.2020.0921
摘要

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.
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