Reputation Distribution and ConsumertoConsumer Online Auction Market Structure A Study in Taobao. com

1、chang boon patrick lee, lingyan maggie zhangreputation distribution and consumer-to-consumer online auction market structure: a study in tchang boon patrick lee, lingyan maggie zhangfaculty of business administration,university of macau, macauemail: cbleeumac.mo; maggie.ld the second international c

2、onference on operations and supply chain management, taipei, jul. 29 to aug. 2, 2008abstract: the purpose of this research was to analyze the sellers reputation scores in the chinese online auction market and relate the results to the market structure. the study examined four sub-markets in taobao n

3、amely, clothing, cosmetics and jewelry, computers and computer accessories, and food and health products. the first three sub-markets are the most popular while the last is the least popular. by using logarithmic transformations and the wald-tests, the results obtained in this study showed that the

4、reputation scores in the four sub-markets did not follow a lognormal distribution. moreover, using the methodology employed by hart and oulten 12, the results found that there were some differences among sub-markets. the paper discussed the implications of the results.keywords: reputation system, ta

5、obao, gibrats law, market structure i. introductionmany years ago, it was difficult for people who havent seen the goods provided by strangers living far afield to carry out business transactions with each other. electronic-commerce (e-commerce), however, helped to overcome this problem by bringing

6、together buyers and sellers. although there are many fraud cases in e-commerce, they have failed to undermine the growth of the consumer-to-consumer (c2c) online auction markets. one reason is possibly the use of online reputation scoring system (or reputation system) to assist users in making trans

7、actions in the internet 23.a reputation system is one that collects, distributes and aggregates feedback about participants past behavior 23. in this way, potential customers can see the reputation scores to assess the risk of trading with the sellers. the online reputation system has attracted rese

8、archers from various disciplines such as the behavioral sciences and economics. from the behavioral sciences perspectives, researchers have focused on the effects of online reputation on trust 3 as well as price 24. in the area of economics, there has been a series of research that investigated the

9、relationship between online reputation and market structure. for example, lin et al. 18 19 used reputation scores as a measure of a firms capacity and analyzed them to study the electronic market structure. lin et al. 18 found that smaller sellers grew faster than larger sellers. their results are n

10、ot consistent with gibrats law of proportionate growth, which posits that a firms growth should follow a lognormal distribution and independent of firms initial size.a review of prior research has found that there is little research on the market structure of c2c auction market in china. as at the e

11、nd of 2007, there were 137 million internet users in china and 23.6 percent of these users had used c2c online shopping services 7. as the chinese economy develops and its social status increases, it is hard to ignore chinas e-commerce market. therefore, this paper focused on taobao a c2c marketplac

12、e which has more than 80 percent of the online auction market share in china 5. this study is based on a large dataset from taobaos reputation system in three best selling categories (clothing, cosmetics and jewelry, computers and computer accessories) and one worst selling category (food and health

13、 products) 6. by using stochastic models such as the gibrats law and the galton-markov model, this paper examined the reputation scores in the sub-markets to address the following questions:1) what is the distribution of the sellers reputation scores in the four sub-markets in taobao?2) what is the

14、difference in the market growth for the four sub-markets? the findings gathered from this paper would provide a market structure profile of the four sub-markets in taobao. they would also provide information about which group of sellers contributes most to the growth of each market and if there is a

15、ny difference in the best and worst selling categories. this paper is organized as follows. the next section examines taobaos reputation system, and this is followed by a review of related literature. we then go on to describe the data collection and the data analysis and results. the paper then end

16、s with a conclusion.ii. taobaos reputation system the name, “taobao”, has a strong cultural meaning which is interpreted as “treasure rush”. it is founded in may 2003 under the flagship of the largest chinese b2b e-commerce company, alibaba. taobao has become the most famous and biggest online c2c a

17、uction market in china today. the success of taobao is due to many things, such as management, innovative service, and cooperation with many partners. a healthy reputation system is also the foundation that contributes to taoboas success. the reputation system in taobao is similar to the reputation

18、feedback forum in ebay. as show in figure 1, the reputation system provides positive, neutral, negative and total scores. figure 1: the reputation system of a store in taobao(source: )taobaos reputation system is different from ebays in three ways. first, taobaos reputation system is divided into tw

19、o parts, one for the seller and the other for the buyer. second, there are scores for one week, one month, six months, and total. third, taobao uses 15 symbols to represent total scores. figure 2 shows the symbols. using these symbols, a buyer can distinguish the reputation of the sellers quickly.fi

20、gure 2: the rating of reputation scores(source: )iii. literature review market structure and gibrats law an important literature related to market structure started with robert gibrats ingalits conomiques 11. gibrat suggested that the expected value of the increment of a firms size in each period is

21、 proportional to the current size of the firm 22. denoting the size of the firm at time t by st and the proportional growth between period (t-1) and period t by a random variable t, so that t = (st - st-1)/ st-1 (1)then st = st-1(1+t) = s0 (1+0)(1+1)(1+t) (2) taking logs and using approximation that

22、 log(1+t) t, then log st = log s0 + 0 +1 +t (3)when t, log s0 become very small compared to log st. by assuming the increment with mean and variance 2, log st can be approximated by a normal distribution with mean t and variance 2t 26. gibrats law or the law of proportional effect pointed out that n

23、o matter the firms initial size, its growth rate follows a lognormal distribution. gibrats law initiated economic research in growth pattern with regard to firm size and revenue distributions for more than 70 years. sutton 27 summarized the “legacy” research developed since gibrats work. this area o

24、f research, called “growth-of-firms” includes research related to verification of proportionate firm size growth 17, stochastic growth model 16 and multi-sectional growth rate of firms among industries 26.although gibrats law provided a basic model for investigating market growth, studies have shown

25、 that it is not satisfactory in explaining the evidence from empirical data. mansfield 21 collected data in three american industries in different time periods and concluded that gibrats law does not satisfy different situations. evans 8 found that firms growth was not independent of firms size smal

26、ler firms grew faster than their larger counterparts. hart and oulton 16 found that for larger firms, there was no relationship between growth and size. according to them, the growth of the firm was affected by large stochastic shocks “which will outweigh the systematic forces in so many cases that

27、the resulting skew size distribution of firms by output will appear to be generated by a multiplicative stochastic process”. their work extended gibrats law by using new enhancements in stochastic models.reputation as an indicator of online business capacitythere are many different economic signals

28、about a conventional firms capacity. previous research has used firm size, revenue, value added, payroll and new capital expenditure to measure firm capacity and had consistent but not identical conclusions regarding growth of a firm 28. for virtual firms in an auction environment, it appears that t

29、here is not much information available regarding the firms. for example, there are many c2c websites that remain anonymous. oftentimes, virtual sellers can register in different names and they buy their own products to increase the reputation scores. to overcome problems related to authenticity of t

30、he firms, many c2c websites use third-party authentication service to assure the information and minimize deceit at the auction market. the adoption of online reputation scores implemented by a trusted third party can help to reduce transaction risk 2. this service helps to increase the validity of

31、the data in the reputation system and is a trend observed in the marketplace.reputation can be regarded as the impression and assessment of a social entitys esteem or desirability 10. bromley 4 pointed out that a social entity builds its reputation based on past behaviors. from the macro level, repu

32、tation has been recognized as an indicator of social stratification 25 and industrial stratification 9. reputation scores, therefore, are perhaps the only publicly accessible measure that can be used as a proxy for the business capacity of a firm 19. this paper, therefore, used reputation scores as

33、the measure of an online firms capacity. reputation scores are also used here to identify the evolution of the general market structure.market structure research in online auction marketlin et al. 18 were among the first to study the relationship between online reputation distribution system and mar

34、ket structure. they conducted a research based on data from eb and found that the reputation scores approximate a geometric distribution. lin et al. 22 then conducted research on the structure and dynamics of electronic markets. the rationale of their method is that reputation can be regarded as the

35、 impression and assessment of a social entitys esteem or desirability. they use reputation scores as the indicator of online business capacity, assuming that each individual trader, a seller in particular, can be treated as a business unit or a virtual firm. an investigation of the transition patter

36、n of trader reputation scores in the c2c online auction market may well represent the evolution of the general market structure. by using the gibrats law and central limit theorem, their proposition leads to the result that the size of a firm is lognormally distributed for sellers. for buyers, the n

37、et reputation data fail to pass the lognormality test, while the six-month total reputation feedback is approximately lognormal. combined with the theory of entry and exit, they explained why the whole reputation system does not follow the gibrats law of proportionate growth but the sellers reputati

38、on distribution is still lognormal. they found that the net population of traders in eb is growing and the total transaction volume is increasing. moreover ebays c2c market is becoming a b2c market, although c2c was an important and main business activity in eb.lin and li 20 had also conducted a com

39、parative study between taobao and ebay. the comparison found that in taobao, (a) the distribution of total reputation scores is lognormal, which is the same as ebay, (b) the negative feedback rates (nfr) and neutral feedback rates (neu) are also lognormal, and (c) the overall neutral feedback rate i

40、s higher than that in ebay. they found the structural change of chinas online auction market in taobao is similar to ebay. in addition, they had some new ideas such as they used the nfr as the best indicator of risks. they found that neutral feedback rates of sellers in both taobao and ebay are logn

41、ormally distributed, while sellers in taobao have a higher overall neutral rate. the results showed that taobaos reputation system can effectively provide information to buyers on the risks of their transactions with a seller.iv. data collectionaccording to the c2c research published by cnnic 6, the

42、 most popular product categories in taobao are clothing, cosmetics and jewelry, and computers and computer accessories. the least popular category is food and health products. rather than studying the reputation system for the whole market, this research focused on the reputation system in these fou

43、r sub-markets. following the data collection procedures reported in lin et al. 19, the data for this study were collected from the taobao website in two periods, in january 2007 and march 2007. for the first period, data for the trader ids and reputation data were collected. the information of trade

44、r ids consists of registered name and opening date of store. the reputation data included positive, neutral and negative reputation scores in recent six months and in total. the study used simple random sampling to collect data for each sub-market. note that we used the equation, n=p*(1-p*)(z/2/e)2

45、1 to determine the appropriate sample size. with p*=0.50, e=3% margin of error, and z/2 =1.96, the sample size, n = 1067. to ensure sufficient numbers, we collected 1500 samples.in the second round of data collection in march 2007, 2500 samples of data, including those contained in the previous 1500

46、 samples were collected. the different sizes of datasets enabled the verification of the consistency of data analysis outcomes at two different time period. the datasets collected at the first and second rounds were labeled 070121 and 070315 respectively. for convenience, the numbers 1 to 4 were use

47、d to represent the different sub-markets. category 1 is for clothing, category 2 for cosmetics and jewelry, category 3 for computer and computer accessories, and category 4 for food and health products. v. data analysis and resultsdescription of data table 1 shows the descriptive data for the sample

48、s collected for this study. for the clothing industry, most sellers had business in the past six months which is about 94.5% and 91.0%, and those who had never sold a single good is very small which is less than 0.2% and 0.3%. the table shows that the tendency of the other three industries is the sa

49、me as the clothing industry. table 1: descriptions of reputation datasetsinitial collection datesample sizetraders whose total reputation scores0traders whose six-month total0123401/21/200701/21/200701/21/200701/21/200711981190150011291196(99.8%)1187(99.7%)1403(93.5%)1116(98.8%)1132(94.5%)1085(91.2%

50、)1261(84.1%)1031(91.3%)123403/15/200703/15/200703/15/200703/15/200723422304230023252336(99.7%)2259(98.0%)2097(91.2%)2311(99.4%)2132(91.0%)2012(87.3%)1860(80.9%)2154(92.6%)histograms of reputation distribution figures 3 to 6 shows the histograms of logarithm-transformed reputation data for this resea

51、rch. in the histograms, y=ln(x), where x is the original reputation data and y is the transformed reputation data. figure 3: histograms of clothing market(a1) total reputation score (jan 07) (a2) 6-month reputation score (jan 07)(a3) total reputation score (mar 07) (a4) 6-month reputation score (mar

52、 07)figure 4: histograms of cosmetics and jewelry market (b1) total reputation score (jan 07) (b2) 6-month reputation score (jan 07)(b3) total reputation scores (mar 07) (b4) 6-month reputation scores (mar 07)figure 5: histograms of computers and computer accessories market(c1) total reputation scor

53、es (jan 07) (c2) 6-month reputation scores (jan 07)(c3) total reputation scores (mar07) (c4) 6-month reputation scores (mar07)figure 6: histograms of food and health products market(d1) total reputation scores (jan 07) (d2) 6-month reputation scores (jan 07) (d3) total reputation scores (mar07)(d4)

54、6-month reputation scores (mar 07)several findings emerged from the histograms. first, for all the four sub-markets, the distributions of the reputation scores deviate from the bell-shaped normal distributions shown with a solid line. second, for the six-month period, all the four sub-markets have a

55、 large number of sellers who had original reputation scores of 1, and log-transformed reputation score of 0. third, for the total reputation scores, the number of sellers with original reputation scores of 1 is much higher in computer and computer accessories market for both time periods. moreover,

56、the number of sellers with original reputation scores of 1 had a great increase in march 2007 in cosmetics and jewelry industry.wald testthe wald test was conducted on the dataset collected at two different time points using the formula,wald-stat=degree_of_freedom*(skewness2/6+kurtosis2/24) where th

57、e critical value 2 (d=2, =0.05) = 5.99, and the degree of freedom is the sample size of the dataset 5. if the wald-stat is less or equal than 5.99, then the distribution of reputation system is lognormal. table 2 shows that for the six-month total reputation score of the clothing industry collected at january 2007, the wald-stat is 2.479 which is les