Currently, China’s e-commerce market is growing at an unprecedented pace, however, it is faced with many challenges, among which the trust fraud problem is the biggest issue. In this article, we use Taobao as an example and conduct a thorough investigation of the trust fraud phenomenon in China’s e-commerce market. We present the development history of trust fraud, summarize its unique characteristics, and explore the reasons why so many sellers commit fraud. We further propose a dynamic time decay trust model that aims to deter trust fraud by raising its cost and promote the growth of small and medium-sized sellers. The model utilizes detailed seller ratings as the data source, and incorporates a transaction amount weight, a time decay coefficient, and three trust factors in the calculation of trust. We test the model on real transaction data from Taobao, and the experimental results verify its effectiveness. Our proposed trust model yields a practical approach to online trust management not only in the Taobao market but also for other e-commerce platforms.
In recent years, China’s e-commerce market is developing very rapidly. The total e-commerce sales increased to RMB5.88 trillion (US$944 billion) in 2011, a year-on-year increase of 129.2%, which accounts for 4.32% of total retail sales in China (The Beijing News 2012). According to an analysis by the Boston Consulting Group, by the end of the year 2015, the size of China’s e-commerce market will possibly surpass that of the U.S. market and become the world’s next e-commerce superpower (Walters et al. 2011).
On the surface, China’s e-commerce market appears to be flourishing, however, it faces many challenges, among which the trust fraud problem is the biggest issue. In this article, we utilize Taobao as an example to carry out an in-depth look at this problem, which has been ignored before. Taobao now is in a prime position, dominating China’s consumer-to-consumer (C2C) market with a 90.3% market share, followed by Tencent’s Paipai with a 9% market share.
To manage trust among buyers and sellers, Taobao has adopted a similar feedback system to eBay. On Taobao, each buyer can offer a feedback rating to the seller after completing a transaction. A positive rating raises a seller’s trust score by one point. A negative rating lowers a seller’s trust score by one point. A neutral rating does not affect a seller’s trust score. The overall trust score is accumulated by adding these ratings together.
Taobao makes use of “heart”, “diamond”, “crown”, and “gold crown” to denote different seller trust levels. (In contrast, eBay uses stars to denote seller feedback scores. As a seller’s feedback score increases, his star will change color.) On Taobao, each level includes a scale of 1 to 5. For example, a trust score of at least 4 earns a seller one heart, and a trust score of at least 251 earns a seller one diamond. If a seller’s trust score reaches 10,001, her trust level will be upgraded to one crown. If her trust score continues to grow and reaches 500,001, then she will receive one gold crown. Thus, the higher the trust score, the more trustworthy will the seller be. This feedback system enables buyers to examine sellers’ previous transactions and provides valuable reference for buyers to make purchase decisions. Reputable sellers are rewarded and may enjoy premium prices and a higher probability of future sales (Brown and Morgan 2006).
Although there are sellers on eBay who manipulate trust by purchasing or exchanging positive feedback ratings (BlackHatWorld, 2010 and BlackHatWorld, 2012), the feedback system has still proven to be quite effective (Brown and Morgan 2006). Taobao has met a big challenge using a similar feedback system though. Compared to eBay, Taobao’s trust fraud problem is much more serious. Here’s why:
Based on the experimental results we have reported, our proposed dynamic trust model supports the following conclusions:
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The closer that a transaction occurred relative to the current time, the larger the weight of its feedback rating will be in the trust calculation.
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By adding the transaction amount weight, we have enlarge the original 5-star rating scale so it has a larger range of 20, which makes it easier for buyers to distinguish among different sellers.
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The average trust score in our proposed model has a defined upper limit and will not grow in an unrestricted manner. All sellers will seek to achieve the upper limit of the trust score, however, it will be very difficult for them to achieve this goal.
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The trust calculation rules in our model are easy to understand and implement.
In the future, we will conduct more complete tests on our dynamic trust model using large-scale data from Taobao. Meanwhile, we will consider more trust factors in the evaluation, such as the history of the online shop, illegal behavior and customer complaints, etc. We also plan to continue our in-depth cooperation with Taobao. We further hope to develop a module based on our proposed dynamic trust model, and apply it in one of Taobao’s online applications to verify its effectiveness and efficiency.
To address the problems with trust in China’s e-commerce, improving the trust model is an important internal solution, while building a social credibility system in China is an external solution. Social credibility, we believe, will have a great impact on the effectiveness of a trust model in China’s e-commerce industry. That is because Taobao’s problems are, in a very real sense, China’s problems (Epstein 2011). Each year, China’s economic loss caused by lack of trust reaches about RMB585.5 billion (US$94 billion) according to statistics of year 2011 (Qian 2011). Everyone in society must pay a high price to compensate for this. The government must intervene and enforce stricter rules, while reminding people of the value of trust in society. When the internal and external solutions function together well, then we will be able to resolve China’s fundamental e-commerce trust issues more effectively.
