Seller Reputation and Price Gouging: Evidence from the COVID ‐19 Pandemic

Algorithmic pricing may lead to more efficient and contestable markets, but high-impact, low-probability events such as terror attacks or heavy storms may lead to price gouging, which may trigger injunctions or get sellers banned from platforms such as Amazon or eBay. This work addresses how such events may impact prices when set by an algorithm and how different markets may be affected. We analyze how to mitigate these high-impact events by paying attention to external (market conditions) and internal (algorithm design) features surrounding the algorithms. We find that both forces may help in partially mitigating price gouging, but it remains unknown which forces or features may lead to complete mitigation.

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Price gouging and the duty of easy rescue

Economics and Philosophy ◽

10.1017/s026626712000036x ◽

2020 ◽

pp. 1-24

Author(s):

Elizabeth Brake

Keyword(s):

Legal Enforcement ◽

Price Gouging ◽

Special Case ◽

Moral Wrongness

Abstract What, if anything, is wrong with price gouging? Its defenders argue that it increases supply of scarce necessities; critics argue that it is exploitative, inequitable and vicious. In this paper, I argue for its moral wrongness and legal prohibition, without relying on charges of exploitation, inequity or poor character. What is fundamentally wrong with price gouging is that it violates a duty of easy rescue. While legal enforcement of such duties is controversial, a special case can be made for their legal enforcement in this context. This account distinguishes, morally, price gouging by corporations from that of individual entrepreneurs.

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Exploring the Effectiveness of Seller Reputation Mechanism Using Online Sales Data

Journal of Electronic Commerce in Organizations ◽

10.4018/jeco.2014040104 ◽

2014 ◽

Vol 12 (2) ◽

pp. 46-58 ◽

Cited By ~ 1

Author(s):

Ying Wang ◽

Lei Huang ◽

Yi Guo

Keyword(s):

Univariate Analysis ◽

Subjective Assessment ◽

Sales Performance ◽

Attribute Selection ◽

Sales Data ◽

Reputation Mechanism ◽

Online Sales ◽

Seller Reputation ◽

Selection Of ◽

Significant Factors

This paper attempts to explore the effectiveness of the seller reputation mechanism by an empirical study using online sales data collected from TaoBao.com. A comparison analysis of seller reputation metrics of TaoBao, Amazon, and jd are carried out before the selection of the seller reputation metrics. The seller reputation metrics of small appliances are used as the input for the study considering the quality homogeneity among different sellers of the market, and the sales performance is measured by the sales amount of the recent month. The univariate analysis are performed to find out the effect of different seller reputation metrics on the sales performance, and the attribute selection technique is then applied to reveal the most significant factors contributing to the sales performance. The result indicates the significance of the user subjective assessment on the sales performance.

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