The Online Auction Market in China - A Comparative Study between Taobao and eBay Zhangxi Lin The Rawls College of Business Administration Texas Tech University Lubbock, TX 79409-2101, USA Tel: +1-806-742-1926, Fax: +1-806-742-3193 Email:
[email protected]
Jun Li Shanghai University of Finance and Economics Shanghai, China Tel: +86-21-6590-4295, Fax: +86-21Email:
[email protected] potential risk to online traders [1],
ABSTRACT
reputation systems available in eBay, Yahoo auction, Amazon and
The success of the online consumer-to-consumer auction is largely
more other online auction marketplaces have contributed
owing to the widely accepted online reputation system. In
significantly to the success. An online reputation system allows
particular, studying the fast growing online auction markets in
traders to rate each other a positive, negative or neutral point after
China, a country with the largest population in the world, will
a transaction. This is then added up to their reputation records that
provide the important outcomes in revealing the trend of global electronic
market.
Encouraged
by the
previous
the prevailing online
are available to future traders as the quantitative indicator of the
research
traders’ online reputation. In addition, traders can leave comments
achievement on eBay’s online reputation system, this paper is
to their trading partners, which will be referred by traders further
intended to report the research findings on the online auction
when needed.
market in China using the online reputation data collected from Taobao - a Chinese version of online auction marketplace. By
The prevailing acceptance and effective operation of online
comparing between the data from Taobao and eBay we found that
reputation system in online C2C auction market have triggered
the distribution of Chinese online traders’ 6-month reputation
wide research interests. The interesting thing is that the research
scores is lognormal; both negative feedback rates and neutral
focus of online reputation shifted in accordance with the growth
feedback rates of Taobao’s reputation data are also lognormally
and acceptance of electronic market. Before year 2000, online
distributed – the same as that of eBay – These are the same as the
reputation research is mainly related to online trust as an important
reputation score distributions in eBay. However, the higher neutral
angle contributing to the e-commerce research when e-market was
scores in Taobao indicate the difference in the cultural aspect
just emerging with huge potentials (e.g. [3]). Later on, as the
contrasting to that of eBay.
online stores became successful, the research interest was turned to the seller side problems, typically, the effect of reputation scores
Keywords
on the price, such as the sixteen representative research projects in Reputation; consumer-to-consumer auction; electronic commerce;
this aspect reviewed in [14]. Later, some research effort has moved
market structure; lognormal distribution
back to the buyer side problems with the focus on the adoption of trusted third party services. For example, Antony et al [2] conduct
1.
an empirical study on the determinants of online escrow service
INTRODUCTION
Powered by eBay’s success, online consumer-to-consumer (C2C)
adoption including online reputation scores. In this research strand
auction as a major e-commerce form has quickly diffused to all
the effect of trader’s perceived risk is identified as an important
over the world in last few years. The great achievement of C2C
determinant in electronic market for purchasing items and
electronic commerce is largely owing to the services of various
adopting trusted third parties’ service, and perceived risk is subject
trust-building systems. In particular, as the asymmetry of
to change with regard to each trading partner’s online reputation.
information in online marketplaces is the main cause of the
Since 2004, Lin et al [12] started to investigate the relationship
1
between online reputation and market structure – a traditional
The unique infrastructure of e-commerce in China, particularly the
economics research issue in industrial organization field. They
online C2C and business-to-consumer (B2C) market, has been
treated the total reputation score of online sellers as the market
shaped and dominated by three major leaders yet competitors in
capacity of firms and revealed that the online reputation scores
the online auction market:
were lognormally distributed, complying to the Gibrat Law well
eBayEachnet, a wholly-owned subsidiary of eBay;
[7].
1pai, a joint-venture company between Yahoo and the Chinese portal company Sina; and
Encouraged by the abovementioned research achievement, this
paper is intended to investigate the online auction market in China,
Taobao, a joint-venture marketplace owned by Alibaba and Softbank.
a nation with the largest population in the world and rapidly
Among the three major players, eBayEachnet and Taobao are
growing Internet population. Right now, e-commerce as a global
about the similar size and lead the online auction market, and 1pai
business trend has also got popular in China, with profound
is at the third place. The following are more detailed introduction
influences in both society and economy, as China has been
to the three e-commerce companies.
experiencing fast growth in economy in the last quarter of the century. Specifically, we are to examine the online reputation
1) EBayEachnet (http://www.ebay.com.cn)
distribution in Taobao.com – a Chinese version of online auction
Shanghai-based EachNet was started in 1999 by two Chinese
marketplace using the data from its reputation system. We
Harvard graduates. In 2004, EachNet claimed to have more than
compare between the data analysis outcomes from Taobao and
10 million users with the transactions of 3 billion yuan (about
eBay with regard to a number of outstanding issues in the online
US$360 million) in merchandise. eBay acquired one third of
reputation system literature.
shares of EachNet in 2002. In 2004 eBay paid US$150 million to
2. ONLINE C2C AUCTION IN CHINA
finally take over EachNet. Since the continual investment in
2.1 An Overview of China’s Online Auction Market
technology platforms and marketing is draining its profitability, eBayEachnet remained unprofitable, although it has been
According to the semi-annual survey conducted by CNNIC (China
experiencing a fast growth in both the trader population and the
Internet Network Information Center, http://www.cnnic.net.cn/),
total transaction volume.
the population of Internet users in China has reached 87 million in July 2004 and the figure jumped to 94 million a half year later.
2) 1pai (http://cn.auctions.yahoo.com)
With the phenomenal growth rate of the online users, the After the debut made in March 2004, 1pai has experienced
development of the E-commerce market has galloped
phenomenal growth with the following months, featured by more
simultaneously (Figure 1).
than 700,000 items transacted in every single day.
Supported by
the source of more than 100 million registered users gathered by Sina and the experienced managerial team dispatched by Yahoo, the online auction marketplace has been completed by merely a quarter of a year. In the consideration of the security for the payment system, 1pai has launched the counterpart to the eBay’s Paypal, named as “1 pai tong”, meaning “One payment for all”. During the fierce struggle between eBayEachnet and Taobao, 1pai just quietly improves its online auction platform and service support strategy. 3) Taobao (http://www.taobao.com)
Figure 1. E-Commerce Growth in China
2
Invested by the largest business-to-business e-commerce company
The following are a few recent most important events about
in China — Alibaba, Taobao (www.taobao.com) was founded in
Taobao:
July 2003. The name "Taobao" has a strong cultural meaning, as is
interpreted as “treasure rush”, based upon the fictional characters
On November 12, 2004 Alibaba started to negotiate with Microsoft on cooperation in the online auction business;
in Chinese historical novels.
Before Taobao launched in the summer of 2003, eBay was the
On December 15, 2004, Taobao and film maker Huayi Brothers reached the agreement on setting up a special
only player in China’s online C2C market. However, Taobao fast
zone on Taobao’s website for Huayi Brothers’ new film
grew from about 9% of the online auction market share in China in
Tian Xia Wu Zei (The World Without Thieves). Many
the first quarter of 2004 to 41% by the fourth quarter of the same
items used in filming the movie are auctioned on
year. According to recent Alexa report (http://www.alexa.com),
Taobao's. According to Taobao, the most popular film
Taobao is ranked the 23rd most popular website in the world,
related items have received up to 10,000 hits.
ahead of all e-commerce sites in China. For providing a vivid view
of the magic development, we collect two histograms to fulfill the demonstration, shown in Figure 2.
On March 3, 2005, Taobao announced that the company will partner with Industrial and Commercial Bank of China to promote Alibaba's third party payment platform Zhi Fu Bao to Chinese e-commerce websites.
The online payment system is undoubtedly the key of Taobao’s innovation, which is titled Alipai (also named in Chinese “zhi fu bao” – meaning “A valuable payment system”) established for the integration of the incisive analysis of the deficiency of the conventional wisdom. It is supposed to have resolved the payment problems that is typically significant in China, such as “Buyer does not want to send money before seeing the goods; seller does not want to ship the goods before receiving money.” Figure 2. Taobao’s Growing Market Value
Alipai is proved to be rewarding for it is responsible for 79% of listings on Taobao. Compared to 21% of listings on eBayEachnet
(Source: iResearch Inc., China Online Auction Report 2004)
transacted via its counterpart, Alipai intrigues the public why the payment system is so successful on Taobao. The report published in April 2004 announced that the growth rate of 768% at Taobao was greater than that of any other domestic website in China. Meanwhile, Taobao is also featured by its No.1 place of other important indexes indicating the quality of the online individual transaction. For instance, the success of the transaction rate locates the top of the list, and nearly quadrupled within twelve months. Nowadays, the real online quantity of the commodity in Taobao has reached nearly 2 million. In general, China’s online auction market can be characterized as
Figure 3. Taobao’s User Population Growth
the follows:
(Source: iResearch Inc., China Online Auction Report 2004)
3
Low penetration of C2C e-commerce at present - only
about 12% of them are regular online shoppers (vs.
2.2 The Reputation System on Taobao
30%+ in the US) – with a rapid growth of e-commerce
Taobao’s reputation system is the most popular and successful one
market
in China. Although it is similar to that of eBay, we can tell the
Localization of market service – e.g. targeting at
differences from Figure 4 that Taobao differentiates its reputation
individual consumers, Taobao poises its marketplace in
information from eBay’s reputation forum by classifying the
Chinese cultural sensibilities to foster a strong user
reputation scores into buying and selling. This makes the
community.
reputation report more informative. A trading partner can justify the trading risk using different types of reputation statistics
The above shows that the online auction market in China has huge
information with regard to the trader’s role in a specific
potential and the difference in culture background and social
transaction.
structure will call for further research to provide insightful guidelines to the market evolution.
Figure 4a. Taobao’ reputation forum
Figure 4b. eBay’s reputation forum (This is the format since January 1, 2004) In general, reputation systems help manage risks and promote
there is no negative feedback [3], and a negative reputation score
cooperation. A strong positive reputation score reduces buyers’
has a greater impact on the ability of sellers to sell their products
risk perception and enhances their trust perception only when
at higher prices than a positive reputation [18]. A seller with a
4
higher negative reputation score is forced to sell his products at a
collected 200 sellers’ records (the dataset is denoted as TB050227)
lower price than will a seller with a lower negative reputation
compared with the 408 records of the same type of data (S-030116)
score [18]. Potential buyers are more sensitive to negative
collected from eBay.com in January 2003. The details about the
feedbacks than positive feedbacks when they plan to buy used or
procedure for the collection of the eBay data are covered in [12].
refurbished products [10]. Therefore, we can expect that the sellers
The Taobao data collection procedure is rather simpler but follows
in the online auction market of Taobao will behave the same way
the same one as used by Lin et al [12] for eBay reputation data
as those in
promote the positive reputation,
collection. In the following subsections, we will present the data
simultaneously, maintain the negative score as little as possible.
analysis outcomes based on the approaches used by Lin et al [12]
Fully aware sellers’ psychological trait, some buyers gouge the
with the extension in the neutral data analysis.
eBay:
to
sellers to reduce the price by threatening the negative scoring.
3.2 Overall statistics of 6-month reputation scores
Paradoxically, the reputation system on Taobao acts like a double-edge sword to reduce the transaction risk as well as
From Table 1, we can identify that:
increase at the same time.
1)
The percentages of sellers who have received feedbacks
Though the above is common to all online reputation systems for
in two datasets, collected respectively from Taobao and
C2C markets, due to their different cultural background of the
eBay, are about the same (94% vs. 97%);
online traders in China, the differentiated design of the reputation
2)
information provision system, and the specialties of e-commerce
negative feedbacks is lower than that of eBay;
environment in China, there must be some subtle differences between the online reputation statistics from that of eBay.
3)
Therefore, we followed the approach used by [11][12][13], to
The percentage of sellers in Taobao having received neutral feedbacks is a little bit higher than that of eBay;
conduct a series of online reputation data analyses. The results are
4)
reported in the follow-up section. 3.
The percentage of sellers in Taobao having received
The average reputation scores the Taobao sellers have received is much lower than that of sellers in eBay.
DATA ANALYSES AND DISCUSSIONS
Most of the above phenomena are consistent to the different seniority levels of online sellers in the two C2C auction sites. The
3.1 Methodology and data collection
unusual situation of neutral scores comparison is to be explored
We investigated the 6-month reputation scores of Taobao.com,
further.
including positive, negative and neutral scores, and randomly
Table 1. Statistical Comparison of Seller Reputation Scores between Taobao and eBay Dataset ID
Collection Date
Dataset
# of sellers
# of sellers
# of sellers
Per
seller
Per seller average
Per
seller
size
with 6-mon
with 6-mon
with 6-mon
average total
negative scores in
average neutral
total
negative
neutral
scores in 6
6 months
scores
scores > 0
scores > 0
scores > 0
months
in
6
months
TB050227
Feb. 27, 2005
200
188 (94.0%)
28 (14.0%)
67 (33.5%)
52.1
0.99
0.20
S-030116
Jan. 16, 2003
408
396 (97.0%)
193 (47.3%)
122 (29.9%)
590.2
5.8
5.3
topic in previous industrial organization research has long been a
3.3 Market structure of Taobao
proliferate field [19]. The traditional approach studies the
Lin et al [12] investigate eBay’s market structure with the
distribution of firm size or revenue to identify a number of
distribution of reputation scores. Market structure as an important
5
interesting issues, such as market’s growth, firm entry and exit,
positive 6-month reputation scores of the Taobao sellers are also
and the market concentration. The most well-known initial
lognormally distributed (Wald-statistics = 2.470). Based on the
research effort is by Robert Gibrat [7], who pointed out that the
proportionate growth assumption, we can hypothesize that the
lognormality of firm size distribution indicated the proportionate
change of sellers’ total reputation scores is proportionate. Figure 5
growth of the market (see the appendix for the information about
compares the histograms of total 6-month reputation scores from
the lognormal distribution). Further work by Kalecki [9], Simon
the logarithms of the two datasets.
and Bonini [16], Singh and Whittington [17], etc. has gradually enriched the market structure theory from different aspects.
Counts (out of 188 samples)
35
However, the traditionally tangible indicators for market structure research, such as firm size and revenue, are difficult to obtain from the online marketplaces because of the nature of anonymity. In order to apply the well-established economic theory to e-market structure research, Lin et al [12] suggest that the online reputation
30 25 20 15 10 5 0
0. 00 0. 49 0. 99 1. 48 1. 98 2. 47 2. 97 3. 46 3. 95 4. 45 4. 94 5. 44 5. 93 6. 44
scores can be used as the measure of firm capacity to study electronic market structure:
Ln(X)
“In the business context, there are many different economic and
(a) TB050327
11
9
10
8
and business scholars have long recognized reputation as an
7
0
based on all past behaviors [4]. At the macro level, sociologists
6
entity, either an individual or a firm, builds his or its reputation
5
assessment of a social entity's esteem or desirability [5]. A social
4
reputation can be basically regarded as the impression and
3
media visibility, and so on [5]. The rationale of our method is that
50 45 40 35 30 25 20 15 10 5 0 2
as marketing information, accounting reports, social responsibility,
1
Counts (Out of 396 samples)
non-economic signals about a conventional firm's capacity, such
Ln(x)
indicator of social stratification [15] and industrial stratification [6], which helps to categorize a person or a firm into different
(b) S-030116 (Source: Lin et al [12])
strata. Similarly, the online reputation also signals the firm’s
Figure 5. The histogram of logarithmic 6-momth total
capacity in the online context.”
reputation scores
Lin et al [12] report that the 6-month total reputation scores of
3.4 Negative and Neutral Feedback Rates
eBay sellers are lognormally distributed, which indicates the
Currently majority reported research work uses positive, negative,
match to the Gibrat’s law. Then the further exploration of the
and net reputation scores as the indicators of online reputation.
research reveals the growth pattern of eBay’s market with regard
Few touched the rate of them so far [11]. Empirical studies on
to different levels of periodic transaction volume.
online reputation are unanimously focused on the effect of seller’s feedback on item prices, although some have investigated the
In light of this research, we used the same approach to study the
effect on the probability of selling products. However, because of
distribution of total reputation scores of Taobao sellers in 6
the diverse nature of the research design, such as different types
months. The Wald-statistics of the logarithm of the total reputation scores is 2.062, which is less than 2(d=2,
α = 0.05)
and categories of items being investigated, different measures of
= 5.99, the critical
reputation, different data collection process, and different sample
value of normality test [8]. Therefore we can not reject the
sizes, no consistent findings were found across these studies.
hypothesis that the total 6-month reputation scores distribution of
Positive feedback might or might not increase price or probability
the Taobao sellers is lognormally distributed. In addition, the
6
of sale, so did negative feedback and net reputation score [15].
2)
The eBay sellers tend to have a higher NFR, while the
However, accumulated positive and negative scores, as well as
Taobao sellers are likely to have a higher NEU.
negative scores in a period of time, were found to be the most
Table 2. Ratios of 6-month Reputation Scores
influencing components of seller’s reputation on buyer behavior.
Dataset
The fundamental point is why online buyers check a seller’s online reputation scores? Clearly, buyers want to estimate the level of risk in transacting with this seller according to the reputation score. Can positive score, negative or net reputation scores tell the likely risk level?
Aver
Posi/
Nega/
Nega/
(Nega+
Total
Total
Total
Total
Neut)/Tot
TB050227
52.1
97.8%
0.37%
1.83%
2.2%
S-030116
590.2
98.1%
0.89%
0.98%
1.9%
Unfortunately, this issue has not been well The interesting issue is why sellers in the two sites behave
investigated. At this point, we argue that the negative feedback
differently. Here we would likely to propose a few possible
rate (NFR) be the best indicator of the risk among all, which is so
reasons for further research.
far not well studied yet. There are several different types of NFRs: overall NFR – the one based on a specific set of traders; life-long
First, this could be owing to the difference in the settings of two
NFR – the ratio calculated from a trader’s life-long reputation
online C2C auction markets. Taobao has a more formal process for
scores; and the NFR obtained from the reputation scores in a
a trader to become a seller – sellers are requested to apply for an
period. From now on, we refer the NFR as the one calculated from
online store first. This extra overhead, though is not high, adds
reputation scores in a 6-month period without specific indication.
some cost to any selling effort, and consequently makes the selling more serious. By February 2005, the total number of sellers
In addition to the work by Li and Lin [11], we are also interested in the ratio of neutral feedback score and total feedback score – the
(online stores) on Taobao is less than 100,000, which is much less
neutral feedback rate (NEU). Traditionally, “Zhong Yong Zhi
than that on eBay.com but they are more dedicated and
Dao” (the moderate approach) has been highly appreciated in
business-oriented. Although eBay also offers formal stores to “professional” sellers, many sellers are occasionally selling their
Chinese culture. This means that instead of “black” and “white”
surplus goods and the selling is relatively easier. If buyers have
rating of a transaction outcome, many Chinese trader may prefer
believed this context, they will tend to deal with sellers as stores,
justifying it as “grey”. Considering the negative rating to a trading
which could be more trustworthy. When they are not fully satisfied
partner may incur a retaliation with the same negative rating which may damage a buyer’s reputation more than the seller’s, the buyer
by a transaction, they simply leave a neutral comment.
might prefer the neutral rating to the seller. Therefore,
Another reason could be the higher overhead to register into
investigating the neutral feedback may identify the main cultural
Taobao. Since cost of
difference between Chinese and non-Chinese online traders.
eBay.com, Taobao buyers typically left neutral feedback instead of
setting up an account is higher than that in
negative one to prevent the retaliation from the seller because a
According to the above discussion, we can see the importance of
high negative score may devalue his/her account and finally he/she
studying NFR and NEU – they provide more information than that
has to discard the account. The accumulated less hostile comments
the positive feedback rate can tell and more influential to traders
will maintain both sides better reputation records.
as they signal the possibility of unpleasant outcomes. Comparing the ratios of different types of reputation scores to the total scores
The third possible reason for the high NEU in Taobao could be
in Table 2, we have the following impressions:
resorted to the differences in the cultural backgrounds of the trader
1)
populations, as we have discussed at the beginning of the section.
The overall 6-month positive feedback rates in Taobao
Current Taobao dataset is not enough for this study. Therefore, we
and eBay are about the same, or alternatively the ratios
leave this issue open for further research.
of negative and neutral scores as a whole to the total scores in Taobao and eBay are about the same.
7
lognormally distributed and so as they are changing proportionally
3.5 Non-positive Reputation Rate Distribution
over time. In addition, this paper found the situations that were not
Li & Lin [11] report that a seller’s NFR is the best indicator for
touched in [12]: The neutral feedback rates of sellers in both
predicting the transaction risk with the seller. This implies that a
Taobao and eBay are also lognormally distributed; and the overall
trader’s NFRs in different periods are correlated. From reputation
neutral feedback rate of Taobao sellers is higher than that of eBay
data analysis, we found that both non-zero negative and neutral
sellers.
feedback rates (the ratio to the total reputation score) of Taobao and eBay sellers fit the lognormal distribution well (see Table 3).
As this primitive research has limited data to further explore the
Since the values of NFR and NEU are in the interval of [0, 1] that
above findings, this paper has identified a few open issues left for
does not fit the domain of lognormal distribution, the lognormal
the research in next phase. First, we will collect the data in two
distribution is just an approximation. If we accept the assumption
time periods to study the change of Taobao’s market structure over
of proportionate change of the rates over time (see the appendix),
time. This will be based on the comparison between two time
we can derive that the unfavorable feedback rates of sellers in
interceptions as well as the data from two time points using the
Taobao and eBay alike, NFR and NEU, tend to be monotonic in
same set of seller IDs. Second, we will design an experiment to
the change direction. That is, the higher NFR or NEU implies the
study the effect of cultural backgrounds of sellers on the reputation
same high level in the next time period. This also means that
feedback behavior and strategy. This will contribute to the
Taobao’s reputation system can effectively provide the information
on-going cross-culture research effort in e-commerce with
to buyers how risky if they are to transact with a seller with regard
different perspectives. Finally, the neutral reputation feedback has
to the seller’s reputation score. We can exclude the increment as
not been tackled yet in recent decade. The exploration in this issue
the overall trend of NFR and NEU because, by intuition, this will
will have the opportunity to contribute to the behavioral theories
result in the distribution of them moving towards 1. The further
with empirical evident.
outcome is that a rational seller will always try to maintain a good
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Technology,
Ideal
Ethical Group
Issues Inc.,
of f (X)
2003,
pp249-266. [14]
Resnick, P., and R. Zeckhauser, “Trust Among Strangers in
1 X Y
1 ln X E[Y] 2 exp 2 Y 2
A.2. Derivation of lognormal distribution
Internet Transactions: Empirical Analysis of eBay's Reputation
System,” in:
Michael
R.B.
(ed.),
In general, the lognormality of a random variable is rooted in the
The
proportionate change of its property. Consider a random variable
Economics of the Internet and E-Commerce (V.11),
X(t), t = 0, 1, …, i-1, i, .., n, with a value X(0) at time 0. X(t)
Elsevier Science, Amsterdam, 2002.
changes in a period of length T with the following four [15]
Shrum, W., and R. Wuthnow, “Reputational Status of
assumptions:
Organization in Technical Systems,” American Journal of Sociology 93 (1988) 882-912.
9
1)
X(i)/X(i-1) = 1 + i, i.e. current value of X is
At the end of the period X(T) = X(0)i=1n(1+i). Applying the
proportional to its value in the last stage.
natural logarithm to both sides of the equation, we obtain: ln(X(T))
2)
Random variable i = i(T/n) are iid
3)
E[i] = T/n, where is a constant
4)
Var[i] = 2/n, where 2 is also a constant
= ln(X(0)) + i=1n ln(1+i). When n, i.e. i0, ln(1+i) i. Therefore, when n ln(X(T)) = ln(X(0)) + i=1n i. According to Central Limit Theorem, i=1n i will converge to a normal distribution. Finally, X(T) is lognormally distributed.
10
