嘈杂的客户评论会打击平台卖家吗?在线太阳能市场的实证分析

Herbie Huang, Nur Sunar, Jayashankar M. Swaminathan, Rahul Roy
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摘要

问题定义:客户评论对在线市场至关重要。然而,评论通常各不相同;产品或服务的评级很少是相同的。在许多服务市场,包括太阳能电池板安装市场,供应方参与者都很活跃。也就是说,卖家必须提出服务每位顾客的建议。在这样的市场中,尚不清楚客户评论的分散如何(或是否)影响市场中卖家的活动水平和匹配数量。我们的论文通过考虑评分和文本评论来检验这一点。据我们所知,这是第一篇实证研究评论分散度如何影响卖家活动水平和活跃卖家在线市场匹配数量的论文。与文献不同的是,我们研究了在线服务市场中评论离散度与供应方活动之间的关系。方法/结果:我们与美国最大的在线太阳能市场之一合作,该市场将潜在的太阳能电池板采用者与安装者联系起来。我们从2013 - 2018年的市场中获得了一个独特的数据集。我们用公共数据集作为补充。我们的分析使用了传统的计量经济学方法、聚类方法和谷歌人工智能开发的基于深度学习的自然语言处理模型BERT。我们发现客户评论的离散度与安装人员的市场活动水平呈显著的倒u型关系。从直觉上讲,市场运营商会倾向于拥有更多拥有完美评级的卖家。相反,我们发现市场层面的审查分散与交易之间存在显著的倒u型关系。管理启示:我们的论文为市场运营商和卖家提供了关键的见解。我们发现,与一般看法相反,运营商可以通过保留/推广低评级卖家或避免(负面)评论过滤来改善其市场交易。此外,卖家为了避免负面评论而实施的“评级门控”可能会减少匹配次数,从而适得其反。补充材料:电子伴侣可在https://doi.org/10.1287/msom.2021.0104上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Do Noisy Customer Reviews Discourage Platform Sellers? Empirical Analysis of an Online Solar Marketplace
Problem definition: Customer reviews are essential to online marketplaces. However, reviews typically vary; ratings of a product or service are rarely the same. In many service marketplaces, including the ones for solar panel installations, supply-side participants are active. That is, a seller must make a proposal to serve each customer. In such marketplaces, it is not clear how (or if) the dispersion in customer reviews affects the seller activity level and number of matches in the marketplace. Our paper examines this by considering both ratings and text reviews. To our knowledge, this is the first paper that empirically studies how the review dispersion affects a seller’s activity level and the number of matches in an online marketplace with active sellers. Distinct from literature, we examine the relationship between the review dispersion and supply-side activities in an online service marketplace. Methodology/results: We collaborated with one of the largest online solar marketplaces in the United States that connects potential solar panel adopters with installers. We obtained a unique data set from the marketplace for 2013 − 2018. We complement this with public data sets. Our analysis uses traditional econometrics methods, a clustering method, and the deep-learning-based natural-language-processing model BERT developed by Google AI. We find that the dispersion in customer reviews has a significant and inverted U-shaped relationship with an installer’s marketplace activity level. Intuitively, a marketplace operator would favor having more sellers with perfect ratings. In contrast, we identify a significant and inverted U-shaped relationship between the market-level review dispersion and transactions. Managerial implications: Our paper provides key insights to marketplace operators and sellers. We find that in contrast to general belief, an operator can improve its market transactions by keeping/promoting sellers with low ratings or avoiding (negative) review filtering. Furthermore, sellers’ implementation of “rating gating” to avoid negative reviews may backfire for them by reducing their matches. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2021.0104 .
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