Information asymmetry and small business in online auction market

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<ul><li><p>Information asymmetry and small business in online auction marketAuthor(s): Chia-Hung Sun and Kang E. LiuSource: Small Business Economics, Vol. 34, No. 4 (May 2010), pp. 433-444Published by: SpringerStable URL: .Accessed: 15/06/2014 13:44</p><p>Your use of the JSTOR archive indicates your acceptance of the Terms &amp; Conditions of Use, available at .</p><p> .JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact</p><p> .</p><p>Springer is collaborating with JSTOR to digitize, preserve and extend access to Small Business Economics.</p><p> </p><p>This content downloaded from on Sun, 15 Jun 2014 13:44:58 PMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>Small Bus Econ (2010) 34:433-444 DOI 10.1007/sl 1187-008-9160-8 </p><p>Information asymmetry and small business in online auction market </p><p>Chia-Hung Sun * Kang E. Liu </p><p>Accepted: 17 November 2008 / Published online: 6 January 2009 Springer Science+Business Media, LLC. 2008 </p><p>Abstract This paper explores how seller reputation affects auction prices using detailed Taiwanese data. Our empirical results show that returns to reputation are nonlinear and differ considerably across different reputation scores. Marginal returns to scores drop sharply after the first reputation quartile, indicating that building up sellers' reputation is extremely important, especially in the early stage. Our study reveals that the mechanism of seller reputations is effective in mitigating asymmetric information in online auctions. </p><p>Keywords Internet auction Buy it now </p><p>Spline regression Taiwan </p><p>JEL Classifications D8 D44 L26 L86 </p><p>1 Introduction </p><p>Small businesses and entrepreneurship are the most important parts of the industrial world, as shown in 2004 whereby more than 99% of all companies in the </p><p>C.-H. Sun (IS) K. E. Liu National Chung Cheng University, Min-Hsiung, Chia-Yi, Taiwan, ROC e-mail: ecdchs@ tw </p><p>USA were small businesses. Entrepreneurship has become a better option for people to increase income on their own and to improve their quality of life. Moreover, the changing economy (globalization, increased competition, and advanced computer tech- nology) fertilizes the business environment for entrepreneurs. The dramatic growth of Internet usage has especially changed consumers' purchasing behavior and hence has created enormous potential opportunities for entrepreneurs. </p><p>The rise of eBay and several other Internet auction sites provides channels for individuals and small businesses to trade goods at real-time market prices around the globe.1 With almost no setup costs, an Internet auction offers instant product exposure and global marketing for entrepreneurial sellers to achieve a great advantage over other marketing strategies. Despite growing at an impressive pace, the increase in Internet scams should come as no surprise. According to Internet Fraud Watch's 2006 report, Internet scams of online auctions (mainly goods never delivered or misrepresented) ranked at the top of overall com- plaints, accounting for 34% of total complaints, and the average loss was US $1,3 3 1.2 </p><p>1 For instance, one online auction site ( is the primary source of income for more than 17,000 Australians, according to The Australian newspaper (March 17, 2007). </p><p>Internet Fraud Watch's website: intset.htm. </p><p> Springer </p><p>This content downloaded from on Sun, 15 Jun 2014 13:44:58 PMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>434 C.-H. Sun, K. E. Liu </p><p>When bidders cannot personally inspect an item's </p><p>quality before bidding in an online auction, low- </p><p>quality and cheaper products (or lemons) may drive </p><p>high-quality products out of markets (Akerlof 1970). Such asymmetric information commonly occurs in used car markets, online auction markets, labor markets (Spence 1973; Harris and Holmstrom 1982), insurance markets (Rothschild and Stiglitz 1976), and credit markets (Jaffee and Russell 1976; Stiglitz and Weiss 198 1).3 To alleviate this informa- tion asymmetry problem, online auction sites (such as </p><p>eBay and Yahoo! Auction) adopt the feedback mechanism of self-enforcement, using the seller </p><p>reputation measured by the number of ratings posted by bidders. How these publicly available com- ments - that is, seller reputation scores - mitigate asymmetric information has become an interesting empirical issue to study. </p><p>A growing number of studies have examined the </p><p>relationship between seller characteristics and auction </p><p>prices in recent economic literature, many of which focus on how seller reputation influences prices when </p><p>asymmetric information exists (McDonald and Slawson 2002; Bajari and Hortacsu 2003; Durham et al. 2004; Dewan and Hsu 2004; Melnik and Aim 2002, 2005). The empirical results, unsurprisingly, support the idea that higher seller reputations increase auction prices, using American Online auction data mostly drawn from eBay. Earlier studies typically assume a linear (or log-linear) relationship between auction prices and seller reputations, leading to a surprisingly small effect on prices. This assumption may be misleading if seller reputations are widely dispersed, as shown in </p><p>Fig. 1; for example, McDonald and Slawson (2002) show that an additional score in reputation increases the price by US $0.04 (for a collector-quality Harley- Davidson Barbie with a mean price of US $263.21), though the returns are likely to become smaller for a </p><p>high-reputation seller. </p><p>Using Taiwanese data from Yahoo! Kimo, this </p><p>study contributes to the literature in the following ways. First, as suggested by Livingston (2005), our </p><p>study incorporates a nonlinear specification of seller </p><p>reputations into the models by dividing seller </p><p>Fig. 1 Scatter plots between scores and auction prices </p><p>reputations into five categories, based on the number of scores. Second, although earlier studies indicate that the influence of the first few positive scores is much greater than that of the later scores (Livingston 2005; Houser and Wooders 2006), no studies analyze the extent of the difference between them. This study applies spline regression, which empirically shows that the returns to reputation differ considerably across different levels of reputation scores. In other words, rather than providing the mean impact of seller reputation on prices in the existing literature, this study offers estimates of the returns to initial reputation scores. Another important feature of this </p><p>study is the division of sample data, which will </p><p>strengthen the findings of the current study, rather than remove buy-it-now (BIN) auctions from the sample, as in previous studies (for example, Anwar et al. 2006; Snir 2006).4 </p><p>The rest of the paper is organized as follows. Section 2 outlines the empirical models and variable descriptions. Section 3 describes data sources. Sec- tion 4 presents the estimation results of the least- squares and spline regressions. Section 5 summarizes the findings and concludes. </p><p>3 Unlike online auction markets, it is difficult to measure sellers' reputation qualitatively, and costly to estimate its impact on prices for financial and commodity markets; for example, insurance and credit markets. </p><p>4 The "buy-it-now" price at eBay also refers to the "buy price" option at Yahoo! Auction or the "take-it-price" at Amazon. Although these terms can be interpreted synony- mously, the function of these options may differ slightly. The BIN price disappears after the first bid is placed at eBay, whereas bidding does not eliminate the BIN price at Yahoo! Kimo. </p><p> Springer </p><p>This content downloaded from on Sun, 15 Jun 2014 13:44:58 PMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>Information asymmetry and small business in online auction market 435 </p><p>2 Econometric analysis </p><p>To examine the determinants of prices in online auctions, the existing literature utilizes various approaches. Empirical studies that include least- squares regression in the analyses include those of Standifird (2001), Ba and Pavlou (2002), Eaton (2002), Bajari and Hortacsu (2003), Durham et al. (2004), Dewan and Hsu (2004), Lucking-Reiley et al. (2007), Dewally and Ederington (2006), and Andrews and Benzing (2007).5 </p><p>Basic intuition suggests that, if an auction attracts a higher number of bids or a seller has a better reputation, then there should be an increase in auction prices. It is possible to test for these effects using the following regression: </p><p>PRICE,- = a0 + </p></li><li><p>436 C.-H. Sun, K. E. Liu </p><p>Table 1 Summary statistics for online auctions (iPod Shuffle) </p><p>Variable N Mean Median Min. Max. </p><p>PRICE (NT $) 466 2,796.4 2,810 2,050 3,650 BIDS 466 7.2 2 1 69 </p><p>OPENBID(NT$) 466 2,100.4 2,500 1 3,600 OPENBIDONE 66 1 1 11 </p><p>OPENBID (&gt;NT $1) 400 2,446.8 2,500 60 3,600 VOLUME 466 6.5 7 1 11 DURATION 466 6.3 7 1 10 </p><p>Mean Std. dev. Median Min. Max. </p><p>Beginner# (N = 10) First quartile (N = 112) </p><p>SCORE 6.35 4.31 6 1 14 NEGSCORE 0.05 0.22 0 0 1 </p><p>Second quartile (N = 116) SCORE 26.16 8.20 25 15 41 NEGSCORE 0.27 0.66 0 0 4 </p><p>Third quartile (N = 113) SCORE 79.92 22.21 73 42 115 NEGSCORE 0.40 1.10 0 0 9 </p><p>Fourth quartile (N = 115) SCORE 464.94 751.72 207 116 4,982 NEGSCORE 1.15 2.99 0 0 28 </p><p>All observations (N = 466) SCORE 141.08 416.82 41 0 4,982 NEGSCORE 0.46 1.67 0 0 28 </p><p>Variable N Mean </p><p>Bids Price (NT $) </p><p>OPENBID OPENBID = NT $1 66 29.3 2,807 OPENBID &gt; NT $1 400 3.6 2,795 </p><p>BONUS 1 40 7.3 2,942 0 426 7.2 2,783 </p><p>BIN 1 208 3.1 2,894 0 258 10.5 2,718 </p><p>MONTH October 144 7.4 2,772 November 156 6.4 2,803 December 166 7.4 2,812 </p><p>DURATION (days) 1 60 2.0 2,806 2 38 3.6 2,811 3 30 9.5 2,808 </p><p>} Springer </p><p>This content downloaded from on Sun, 15 Jun 2014 13:44:58 PMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>Information asymmetry and small business in online auction market 437 </p><p>Table 1 continued </p><p>Variable N Mean </p><p>Bids Price (NT $) </p><p>4 31 12.2 2,864 5 33 5.8 2,874 6 30 7.3 2,836 7 48 9.9 2,769 8 22 4.1 2,785 9 23 7.7 2,846 10 151 8.5 2,770 </p><p>WEEKEND 1 109 7.8 2,788 0 357 6.8 2,802 </p><p>Bidding type N OPENBIDONE OPENBID &gt; NT $1 Mean </p><p>OPENBID (NT $) BIDS PRICE (NT $) </p><p>BT! 73 0 73 2,610 1.0 2,716 BT2 61 1 60 2,505 1.0 2,928 BT3 95 0 95 2,822 1.0 2,869 BT4 185 49 136 1,601 14.2 2,718 BT5 52 16 36 1,370 9.3 2,901 </p><p>respectively. Seller reputation is not directly obser- vable, but it can be measured by the number of positive (or negative) scores posted by bidders. The mean positive score for all sellers is 141.53, in contrast to a small mean negative score of 0.46. There are 66 auctions with a starting bid of NT $1, and they receive an average of 29.3 bids per auction, contrast- ing with the remaining 400 auctions with an average of 3.6 bids per auction. The mean score ranges from 6.35 in the first quartile to 464.94 in the fourth quartile, and the average negative score is very small, although it increases slightly from 0.05 to 1.15. Unlike many electronic goods, the mean price for an iPod Shuffle does not fall during the sample period. </p><p>Another increasingly popular feature of online auctions offered by eBay and other online auction sites is called "buy it now."8 It allows buyers to end </p><p>the auction early at a predetermined price, and people who are impatient or risk averse may find this function beneficial. The average price of auctions ended with BIN is NT $2,894, which is relatively higher than non-BIN auctions by roughly 6.5% or NT $176, and the impact of BIN persists for the following bidding types: BT2, BT3, and BT5. How- ever, BT4 has the highest average bids of 14.2 per auction, but its mean price is reported to be the second lowest at NT $2,718. Figure 2 shows scatter- plots between scores and auction prices by bidding types, illuminating some of the factors of interest in studying these online auctions. Finally, auctions ending during the weekend draw more bids than weekdays, and auctions ending on day four have the highest average bids of 12.2 and a price of NT $2,864. </p><p>8 The theoretical explanations of BIN' s impact on an auction price from the viewpoint of sellers have been analyzed by Budish and Takeyama (2001), Mathews (2004), and Reynolds and Wooders (2009). For instance, Budish and Takeyama (2001) explain that, when bidders are risk averse, an optimally set BIN price can raise the expected profits of the seller. Our </p><p>Footnote 8 continued empirical examination does not include the risk attitude of bidders as a variable in the regression, because risk measure- ment is unavailable from the online auction sites. </p><p> Springer </p><p>This content downloaded from on Sun, 15 Jun 2014 13:44:58 PMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>438 C.-H. Sun, K. E. Liu </p><p>Fig. 2 Scatter plots between scores and auction prices by bidding types </p><p>4 Empirical results </p><p>Table 2 reports the empirical results of the estimation of equation (1). Parameter estimates of SCORE are positive and statistically significant at the 5% level in models 1-2. Our finding that reputation is positively related to prices is consistent with the existing literature. In line with Ba and Pavlou (2002) and Bajari and Hortacsu (2003), NEGSCORE (or NEG- ATIVE) does not produce any significantly negative impact on auction prices.9 The insignificant coeffi- cients of NEGSCORE indicate that bidders do not penalize sellers with a few negative feedbacks. Two possible explanations are offered. First, the fear of retaliation tends to reduce the frequency of negative comments, leading to the insignificant impact of NEGSCORE. Second, if a seller has received several negative feedbacks, then there is an incentive for the seller to obtain a new account to continue the online auction business. As a result, the number of negative scores recorded in the dataset may not truly reflect the overall sellers' negative reputation. </p><p>The estimates for the coefficients of BIDS, OPENBID, and OPENBIDONE are positive and statistically significant at the 1% level, suggesting that auctions with a higher opening bid generate higher auction prices. The effects of MONTH and WEEKEND on prices are insignificant. As expected, the number of quantity supplied, VOLUME, nega- tively impacts auction prices, which is consistent with fundamental economic principles, that is, large sup- ply lowers prices. Finally,...</p></li></ul>


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