Incorporating the Time-Order Effect of Feedback in Online Auction Markets through a Bayesian Updating Model

MIS Q. Pub Date : 2021-06-01 DOI:10.25300/misq/2021/15324
M. Chau, Wenwen Li, Bo Yang, Alice J. Lee, Z. Bao
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引用次数: 2

Abstract

Online auction markets host a large number of transactions every day. The transaction data in auction markets are useful for understanding the buyers and sellers in the market. Previous research has shown that sellers with different levels of reputation, as shown by the ratings and comments left in feedback systems, enjoy different levels of price premiums for their transactions. Feedback scores and feedback texts have been shown to correlate with buyers’ level of trust in a seller and the price premium that buyers are willing to pay (Ba and Pavlou 2002; Pavlou and Dimoka 2006). However, existing models do not consider the time-order effect, which means that feedback posted more recently may be considered more important than feedback posted less recently. This paper addresses this shortcoming by (1) testing the existence of the time-order effect, and (2) proposing a Bayesian updating model to represent buyers’ perceived reputation considering the time-order effect and assessing how well it can explain the variation in buyers’ trust and price premiums. In order to validate the time-order effect and evaluate the proposed model, we conducted a user experiment and collected real-life transaction data from the eBay online auction market. Our results confirm the existence of the time-order effect and the proposed model explains the variation in price premiums better than the benchmark models. The contribution of this research is threefold. First, we verify the time-order effect in the feedback mechanism on price premiums in online markets. Second, we propose a model that provides better explanatory power for price premiums in online auction markets than existing models by incorporating the time-order effect. Third, we provide further evidence for trust building via textual feedback in online auction markets. The study advances the understanding of the feedback mechanism in online auction markets.
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基于贝叶斯更新模型的在线拍卖市场反馈时序效应研究
网上拍卖市场每天都有大量的交易。拍卖市场的交易数据对了解市场上的买卖双方很有帮助。先前的研究表明,不同声誉水平的卖家(如在反馈系统中留下的评级和评论)在交易中享有不同水平的价格溢价。反馈分数和反馈文本已被证明与买家对卖家的信任水平和买家愿意支付的价格溢价相关(Ba和Pavlou 2002;Pavlou and Dimoka 2006)。然而,现有模型没有考虑时间顺序效应,这意味着最近发布的反馈可能被认为比最近发布的反馈更重要。本文通过(1)检验时间顺序效应的存在性,以及(2)提出一个考虑时间顺序效应的贝叶斯更新模型来表示买家感知声誉,并评估它如何很好地解释买家信任和价格溢价的变化。为了验证时间顺序效应并评估所提出的模型,我们进行了用户实验并收集了eBay在线拍卖市场的真实交易数据。我们的研究结果证实了时间顺序效应的存在,并且所提出的模型比基准模型更能解释价格溢价的变化。这项研究的贡献有三个方面。首先,我们验证了在线市场价格溢价反馈机制中的时间顺序效应。其次,我们提出了一个模型,该模型通过纳入时间顺序效应,为在线拍卖市场的价格溢价提供了比现有模型更好的解释力。第三,我们为在线拍卖市场通过文本反馈建立信任提供了进一步的证据。该研究促进了对在线拍卖市场反馈机制的理解。
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