Mathematical Modeling of Insurance Mechanisms for E-commerce Systems

TL;DR

This paper develops a stochastic model for e-commerce reputation systems, introduces an insurance mechanism to improve seller onboarding and profitability, and demonstrates significant enhancements in key performance metrics.

Contribution

It proposes a novel insurance mechanism for e-commerce reputation systems, with analytical validation showing substantial improvements in seller ramp-up time and profits.

Findings

Reduces seller ramp-up time by approximately 87.2%.

Ensures new sellers ramp up before deadline with high probability.

Increases long-term profit gains and transaction gains by over 95%.

Abstract

Electronic commerce (a.k.a. E-commerce) systems such as eBay and Taobao of Alibaba are becoming increasingly popular. Having an effective reputation system is critical to this type of internet service because it can assist buyers to evaluate the trustworthiness of sellers, and it can also improve the revenue for reputable sellers and E-commerce operators. We formulate a stochastic model to analyze an eBay-like reputation system and propose four measures to quantify its effectiveness: (1) new seller ramp up time, (2) new seller drop out probability, (3) long term profit gains for sellers, and (4) average per seller transaction gains for the E-commerce operator. Through our analysis, we identify key factors which influence these four measures. We propose a new insurance mechanism which consists of an insurance protocol and a transaction mechanism to improve the above four measures. We show that our insurance mechanism can reduce the ramp up time by around 87.2%, and guarantee new sellers ramp up before the deadline $T_w$ with a high probability (close to 1.0). It also increases the long term profit gains and average per seller transaction gains by at least 95.3%.