4 Insights And

National Central University

Dept. of I.M.

Impact of information on bidding price for online auction

Advisors: Yi-wen Fan Student: Richard Chen

Date: June. 2004

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: Impact of information on bidding price for online auction

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eBay Yahoo

Internet 2003/10/01 2004/04/01


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Abstract A growing number of organizations and individual sellers are selling products or

right of service through the online auction markets, such as eBay or Yahoo. Online

auction is a new way of exchange products or rights of service by all participants

through the Internet. Sellers wish the final closing price can be settled as high as

possible so that they can maximize their profit from these online auction transactions.

However, it is not clear to both the academic researchers and the practitioners what

influences the final closing price in online auction transactions. This research aims

to investigate the impact of information on bidding price for online auction. Based

on the sample data collected from the real auction transactions from 2003/10/01 to

2004/04/01, the results of this research indicate that the final closing price for online

auction is affected by the sellers good reputation, sellers bad reputation, picture

quality of merchandise and number of bids. The number of bids is affected by sellers

good reputation, sellers bad reputation, and picture quality of merchandise.

Keywords: Online auction, bidding price, information


III

Table of Contents 1. Introduction............................................................................................................1 2. Literature Review...................................................................................................3

2.1. Auction the market.....................................................................................3 2.2. Auction categories .....................................................................................3 2.3. The relative research ..................................................................................4 2.4. eBay and online auction.............................................................................9 2.5. Online auction in Taiwan .........................................................................10 2.6. The operation of online auction ...............................................................11

3. Research methodology.........................................................................................14 3.1. Research framework ................................................................................14 3.2. Hypothesis summary................................................................................14 3.3. Variables and hypothesis..........................................................................15 3.4. Control variables......................................................................................20 3.5. Dependent variable ..................................................................................21 3.6. Data collection method ............................................................................21

4. Data Analysis .......................................................................................................27 4.1. Status and auction performance ...............................................................27 4.2. Reputation and auction performance .......................................................29 4.3. Picture quality and auction performance .................................................36 4.4. Number of bids and auction performance................................................42 4.5. Reputation and number of bids ................................................................48 4.6. Picture quality and number of bids ..........................................................53 4.7. Adjusted model ........................................................................................56 4.8. Adjusted model for brand new merchandises..........................................58

5. Conclusion ...........................................................................................................59 5.1. Conclusion and findings ..........................................................................59 5.2. Comparison with previous research.........................................................60 5.3. Managerial implication ............................................................................62 5.4. Research limitation ..................................................................................62 5.5. Suggestion for future research .................................................................63

References....................................................................................................................65


IV

Table Index Table. 2.2-1 comparison of different auction type .........................................4 Table. 2.5-1 Number of auction items of Yahoo and eBay ..........................10 Table. 3.2-1 Hypothesis list .........................................................................15 Table. 3.3-1 result of pilot survey. ...............................................................18 Table. 4.1-1 ANOVA result of status and auction performance...................27 Table. 4.1-2 the average auction performance of each status ......................28 Table. 4.2-1 ANOVA good reputation and auction performance.................29 Table. 4.2-2 ANOVA bad reputation and auction performance ...................30 Table. 4.2-3 ANOVA bad reputation and good reputation...........................32 Table. 4.2-4 ANOVA good reputation and auction performance (only brand

new)......................................................................................................33 Table. 4.2-5 ANOVA bad reputation and auction performance (only brand

new)......................................................................................................34 Table. 4.2-6 ANOVA good reputation and bad reputation (only brand new)

..............................................................................................................35 Table. 4.3-1 ANOVA picture quality and auction performance...................36 Table. 4.3-2 ANOVA good reputation and picture quality...........................37 Table. 4.3-3 ANOVA bad reputation and picture quality.............................38 Table. 4.3-4 ANOVA picture quality and auction performance (only brand

new)......................................................................................................39 Table. 4.3-5 ANOVA good reputation and picture quality (only brand new)

..............................................................................................................40 Table. 4.3-6 ANOVA bad reputation and picture quality (only brand new) 41 Table. 4.4-1 ANOVA number of bids and auction performance..................42 Table. 4.4-2 ANOVA number of bids and auction performance (without

direct-buy price)...................................................................................43 Table. 4.4-3 ANOVA number of bids and auction performance (with

direct-buy price)...................................................................................44 Table. 4.4-4 ANOVA number of bids and auction performance (only brand

new)......................................................................................................45 Table. 4.4-5 ANOVA number of bids and auction performance (brand new

and without direct-buy price)...............................................................46 Table. 4.4-6 ANOVA number of bids and auction performance (brand new

and with direct-buy price)....................................................................47 Table. 4.5-1 ANOVA bad reputation and number of bids............................48


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Table. 4.5-2 good reputation and number of bids ........................................49 Table. 4.5-3 ANOVA good reputation and number of bids (only brand new)

..............................................................................................................50 Table. 4.5-4 ANOVA of bad reputation and number of bids (only brand new)

..............................................................................................................52 Table. 4.6-1 ANOVA picture quality and number of bids............................53 Table. 4.6-2 ANOVA of picture quality and number of bids (only brand new)

..............................................................................................................54 Table. 4.7-1 Hypothesis testing result..........................................................56 Table. 4.8-1 Hypothesis testing result (only brand new) .............................58


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Figure Index Fig. 3.1-1 Research Framework...................................................................14 Fig. 3.6-1 Example of tree structure of online auction site..........................23 Fig. 4.1-1 plot of status and auction performance .......................................28 Fig. 4.2-1 plot of good reputation and auction performance .......................30 Fig. 4.2-2 plot of bad reputation and auction performance..........................31 Fig. 4.2-3 plot of bad reputation and good reputation .................................32 Fig. 4.2-4 plot of good reputation and auction performance (only brand new)

..............................................................................................................33 Fig. 4.2-5 plot of bad reputation and auction performance (only brand new)

..............................................................................................................34 Fig. 4.2-6 plot of god reputation and bad reputation (only brand new).......35 Fig. 4.3-1 plot of picture quality and auction performance .........................36 Fig. 4.3-2 plot of good reputation and picture quality .................................37 Fig. 4.3-3 plot of bad reputation and picture quality ...................................38 Fig. 4.3-4 plot of picture quality and auction performance (only brand new)

..............................................................................................................39 Fig. 4.3-5 plot of good reputation and picture quality (only brand new).....40 Fig. 4.3-6 plot of bad reputation and picture quality (only brand new).......41 Fig. 4.4-1 plot of number of bids and auction performance ........................42 Fig. 4.4-2 plot of number of bids and auction performance (without direct

buy price) .............................................................................................43 Fig. 4.4-3 plot of number of bids and auction performance (with direct-buy

price) ....................................................................................................44 Fig. 4.4-4 plot of number of bids and auction performance (only brand new)

..............................................................................................................45 Fig. 4.4-5 plot of number of bids and auction performance (brand new and

without direct-buy price)......................................................................46 Fig. 4.4-6 plot of number of bids and auction performance (brand new and

with direct-buy price)...........................................................................47 Fig. 4.5-1 plot of bad reputation and number of bids ..................................48 Fig. 4.5-2 plot of good reputation and number of bids ................................49 Fig. 4.5-3 plot of good reputation and number of bids (only brand new)....51 Fig. 4.5-4 plot of bad reputation and number of bids (only brand new)......52 Fig. 4.6-1 plot of picture quality and number of bids ..................................53 Fig. 4.6-2 plot of picture quality and number of bids (only brand new)......55


VII

Fig. 4.7-1 adjusted model.............................................................................56 Fig. 4.8-1 adjusted model (only brand new) ................................................58


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1. Introduction

People are becoming more aware of and more interested in the Internet and

e-commerce all over the world. Such interest can be observed by the increasing

sales of e-commerce. According to Forrester Research, the world-wide e-commerce

sales was estimated to reach USD6.79 trillion in 2004Forrester Research, 2004. This estimate is about three times the USD2.23 trillion e-commerce sales in 2002.

Online auction is one of the most growing e-commerce activities. Online auction

sales are estimated to account for 25 percent of total online retail sales in the US by

2007, according to Forrester Research. The research company predicts that online

auction sales will grow at a compound annual growth rate (CAGR) of 33 percent over

the next few years. The total online auction sales will rise from USD13 billion in

2002 to USD54 billion in 2007, according to Forrester. Research also indicates that

only a small percentage of North Americans have purchased goods from online

auctions in the past. However, Forrester predicts that mainstream shoppers will drive

online auction sales in the future.

With the growth of online auction market, growing number of organizations and

individual sellers are selling products or right of service through the online auction

markets, such as eBay or Yahoo. Online auction is a new way of exchanging

products or rights of service by all participants through the Internet. Online auction

is quite different from the traditional auction because of the possibility of being

anonymous for all participants. It is not easy to identify whom the sellers or the

buyers really are for the online auctions. Information provided by the auction

market becomes critical for buyers to decide whether to attend the online auction

process or at what price to bid.

In order to maximize the profit, sellers wish the final closing price can be settled

as high as possible during these online auction transactions. However, it is not clear

to both the academic researchers and the participants what influence the information

has on the final closing price in any online auction. This research aims to investigate


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the impact of reputation information on bidding price for online auction.

For regular long-term sellers, its vital to have feasible selling strategies. But if

they sell goods over online auction, the strategy is more important. They want to gain

more via correct selling strategy. But there are too many uncertain factors which

affect the result. One objective of this research is to find out what is important for

sellers.

This section describes the motivation of this research. The introduction of

auction concepts and relative literature for online auction will be presented in next

section. Then, research design and data analysis follows. Finally, the research

finding is concluded in the last sections.


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2. Literature Review

2.1. Auction the market Auctions have long been of special interest to economists because they are

explicit mechanisms that describe how prices are formed (Hendricks & Paarsch,

1995). These markets provide centralized procedures for the exposure of purchase

and sale orders to all market participants simultaneously.(Lee , 1996)

Auctions are formalized trading procedures in which the trading partners

interaction is governed by specific trading rules. In many cases an (online) auctioneer

is functioning as an intermediary. Online auctions are a special case of automated

negotiations (cf. Beam; Segev 1997). The auction patterns vary with the trade objects

and trade rules. They cover extremes such as auctions for commodities like financial

products, metals or agricultural products on the one side and auctions for unique items

of fine art on the other.

2.2. Auction categories Auctions have a long history in human society. An auction is a market

institution with an explicit set of rules determining resource allocation and prices on

the basis of bids from the market participants (McAffee & McMillan, 1987, p. 701).

Such mechanism sets out rules for bidding and allocates the goods to a certain bidder

based on the predefined rule set (Klein, 1997; Segev & Beam, 1998). The auction can

be treated as a resource allocation mechanism for buyers and sellers to compete on

Price for specific products (Bierman & Fernandez, 1993) because the major job of

negotiating is often price alone.

There are various auction types in the market. The process is different between

various auction types. They can be divided generally into sealed-bid and open

auctions. Bichler and Segev (1998) and Ravi et al (2001) summarize the auction types

in each category:


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Table. 2.2-1 comparison of different auction type

Auction Rules Result

Sealed Auctions

First priced sealed bid

Bidders submit a single sealed bid before deadline.

Winner is highest bid, and they pay what they bid.

Vickrey Bidders submit a single sealed bid before deadline.

Winner is highest bid, and they pay the second high bid price.

Sealed double auction

Bidders and sellers submit a single sealed bid before deadline.

Auctioneer determines a single market-clearing price and matches buyers and sellers.

Open Auctions

Dutch Auctioneers calls out descending price; bidder calls out a bid.

Winner is first bidder to call out, at bid price.

English Bidders successively raise bids for item until single bidder remain.

Winner is last bidder remaining. The price may be at bid price or second highest bidder.

In this study, the term of online auction means auctions conducted on the

Internet. Although most online auctions conducted on the Internet adopt traditional

auction type, due to the limitation of Internet, most of online auctions are based on the

rule of English Auction. Slight modification may be applied, but the essential of

auction type is constant. Consequently, this paper only focused on Online English

Auction, and this style is generally applied on almost all online auction sites in

Taiwan which we will discussed later.

2.3. The relative research Although auction appears on earth long time ago, and the researches about it are

numerous. The theoretical work has focused mostly on how bidders should behave,

what they should bid, and the functions of different auction mechanisms.


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Wilcox (2000) investigated the effects of experience on bidding behavior in

auctions on eBay for several product categories: DeWalt brand hand-held power drills,

designer name mens neckties, desk-top staplers, and Rookwood Pottery vases. He

examined whether experience affects the behavior of bidder. The studys findings

revealed that more experienced bidders are more likely to place their bids during the

final minute of the auction. For products where the value is less certain, such as ties

and pottery, more experienced bidders are less likely to make multiple bids.

Dholakia and Soltysinski (2001) examine the existence of what they term

herd behavior bias in on-line auctions. This phenomenon occurs when bidders tend to bid for auction listings with more bids and ignore comparable or even better

listings with no bids, during the same time period. According to their research, some

items become coveted by attracting many bidders, whereas other items that might be

comparable or even superior remain overlooked. Results from their on-line auction

study revealed that the effects of the herd behavior bias decrease when the price of the

item increases, but the effects of the herd behavior bias increase when quality of is

difficult to evaluate.

Internet auctions have also received some research attention in other areas, such

as sociology. For example, Brinkman and Siefert (2001) analyzed eBay to

empirically verify that trust is necessary to overcome the risks of these types of

transactions. They discovered that the existence of the feedback system on the

auction site forms the foundation of a high-trust context. In addition, the study

showed that a loss of trustworthiness (negative feedbacks) reduces both the buyer and

sellers chances of participating successfully in the auction community.

Although on-line auctions are attracting to see some research attention, there

remain many interesting questions. One important question relate to what influences

final closing price. This issue is of utmost importance to sellers given that sellers

want items they offer for sale to ultimately be sold at the highest price possible.

James and Kristy (2003) investigate how particular on-line auction features impact

two important outcomes: auction success and final closing price. Their research


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examines the results of auctions for sterling silver flatware conducted on eBay, and

they find the reserve auction format, the relative opening price, and the number of

bids unexplained by a low or high opening price is associated with both auction

success and final closing price.

The reference price

In the market place of online auction, the seller and the buyer all negotiate just

what they want. The seller want to raise the price, the buyer want to get the

merchandise at a reasonable price. If you want to sell higher, you must make buyer

fell that it is worth buying. Sangman et al (2001) had indicated that buyer will feel

gain if the price is lower than reference price, and feel loss if the price is higher than

reference price. That is, if the seller wants his merchandise to be bided higher, he must

let buyer fell gained. The reference prices become an important metric in online

auction.

Reference price, which is based in part on the past pricing activity of a product,

is stored in a consumers memory and serves as a point of comparison for future

purchases.

In traditional retailing market, price has a significant influence on consumers purchase behavior and consequently on firm sales and profit. And changes from a

base or reference price are likely to have an impact only when the price change is

above a threshold. Price thresholds are influenced by company, competitor and

consumer factors. (Sangman et al, 2001)

Why understand price thresholds?

1. It helps a manager decide the minimum price discount needed to

have any impact on consumer choice. And helps retailers negotiate

the appropriate level of promotional discount with manufacturers.

2. It provides a useful method of customer segmentation based on how


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consumers differ in their price thresholds.

For example, a product with low threshold means its price sensitivity is high. The change of reference price might affects

sales more significantly.

3. Helps a manager to understand and monitor the power of his brand.

Brands with low thresholds for price discounts affect consumer

purchases with a small cut in their price and, therefore, exhibit

greater clout in the market place.

4. It provides an opportunity for a manager to identify and manage

variables that affect price thresholds and, therefore, power of his

brand.

For example, it is possible that frequent discounting of a brand may increase its price threshold such that the same level of

discount is no longer sufficient to influence consumers purchases.

In this study, the price threshold is the gap between the merchandises current bid

and its reference price. What makes seller laugh or cry when the auction is finish? In

other words, the performance of the auction, sell higher or lower relative to reference

price.

Determinants of Internet auction success and closing price

Traditional economic theories as well as theories from marketing and psychology

are employed to provide a broader picture of on-line auctions. James H. G. and Kristy

R, 2003, found the determinants of Internet auction success and closing price. In the

study, several key factors related to auction success and closing price for four types of

sterling flatware in an on-line auction site (eBay) is examined. The findings show that,

across all four piece types, a reserve auction format, the relative opening price, and

the number of bids unexplained by a low or high opening price are associated with


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both auction success and final closing price.

The eBay auctions examined in the research project are for sterling silver

flatware, specifically four different piece types cold meat forks, gravy ladles, sugar

shells (spoons), and teaspoons in three different patterns from the Gorham

Company Buttercup, Chantilly, and Strasbourg. Each of these patterns was first manufactured in the 1890s 1899, 1895, and 1897, respectively and remains in production today. Although the choice of product to study is somewhat arbitrary,

sterling flatware was chosen as an example of a durable product with easily seen and

described damage that does not, by nature, lose value with age. The patterns chosen

are still among the most popular with a long production history that ensures high

volume in the secondary market. The piece types chosen are three common serving

pieces, along with the most common place-setting piece. Care was taken to avoid

pieces such as knives, forks, or salad forks, for which size (e.g., lunch vs. dinner size)

plays a significant role in price, as sizes are often not provided in descriptions

The research uses logistic regression to analyze eBays online transaction data.

Consequently, a reserve auction format, the relative opening price and the number of

bids are important determinants of auction success. Then, a liner regression was

performed and the research uses average final closing price as dependant variable. It

shows that these variables are also associated with final closing price.

But its a pity the finding is only for sterling silver flatware. There are numerous

merchandise of different categories were traded over online auction market. It is hard

to analyze when the sample data is mixed with different merchandise. For example, if

the sample data contains many daily commodities and some cars of Mecedz Benz.

The average final closing price will be impacted because of these cars. The reference

price of Mecedz Benz is relatively high and makes the average final closing price

unable to describe each data. To solve this problem, this research propose that final

closing price and reference price are combined as dependant variable. This method

will be discussed on next section 3.


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2.4. eBay and online auction As the Internet grows rapidly, traditional transaction mechanisms like auctions

have been imported to the online world.(Wang K. & Wang E.T.G. & Chi-Feng T. 2002)

People have been able to place bids at traditional auctions via the Internet for a couple

of years but with the advent of Internet-only auctions, the computer has become the

auction house. And basically anyone with something to sell can be an auctioneer at

any time, any place.

Founded in September 1995, eBay (Nasdaq: EBAY; http://www.eBay.com) is an

online auction market place for the sale of goods and services by a diverse community

of individuals and businesses. Today, the eBay community includes tens of millions of

registered members from around the world. People spend more time on eBay than any

other online site, making it the most popular shopping destination on the Internet.

eBay has surpassed Amazon.com in the battle for online traffic, according to

Nielsen NetRatings. With 22.13 million visitors in March, 2001, up 4 million from

February, eBay is now the most visited online retailer. Amazon had 22.08 million

visitors in March, up 1 million from February. eBay also outscores Amazon in terms

of pages viewed and average time spent per visitor to the site. Nielsen NetRatings also

found that the number of US at-home Internet users rose by 7 million in March to 211

million. (May 02 2001, Nielsen NetRating)

Items for sale are posted on its Web site, with photos and descriptions. Bids are

updated constantly so buyers can watch the activity in real time. The auctions run

over three, five or seven days, offering everything from antiques, toys and coins to

items in a special Elvis site called 'All The King's Things.'

Other specialty auction houses include naturalhistoryauction.com, in Ithaca, NY,

which deals in minerals, fossils, dinosaurian and meteorites. Its inaugural auction in

August, 1998 featured a piece of the moon and a hadrosaur egg. Those interested in

car auctions can log on to littlegarage.com where the owner of the Little Garage puts

would-be-sellers in contact with would-be-buyer


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Auction fraud accounts for nearly 43 percent of all reported Internet fraud in the

US, reports Internet.com. This is according to a report from the Internet Fraud

Complaint Center (IFCC). Non-deliverable merchandise and non-payment

accounted for nearly 20.3 percent of complaints to the IFCC in 2001. The Nigerian

letter fraud made up 15.5 percent of complaints, while complaints regarding

credit/debit card fraud and confidence fraud, were also reported. The IFCC received

over 49,711 complaints from Internet users during 2001. Most of the complaints

received involved computer intrusion, hacking and child pornography, rather than

Internet fraud. Over 64 percent of all consumers spending at auction sites went to

eBay.com, while 14.7 percent went to uBid.com. Egghead.com, Yahoo auctions and

Amazon auctions completed the top five sites, each with less than 4 percent of all

online auction revenues for the month. eBay also had the highest satisfaction rate

among users and the highest conversion rate. (IFCC, Apr 10 2002)

2.5. Online auction in Taiwan eBay is the biggest online auction site worldwide. eBay operates in 27 countries,

and it charges on each auction transaction excepts Taiwan. eBays service is free in

Taiwan, but the biggest one is still Yahoo!Auction Taiwan. (www.eBay.com.tw, 2004)

There are two competing advantage owned by Yahoo!Auction Taiwan. First, it is

the biggest Internet portal in Taiwan. Second, Yahoo!Auction Taiwan started earlier

and already had many loyalty users.

The online data show the current auction market status in Taiwan, the number of

auction items of Yahoo! Auction Taiwan is totally greater than eBay Taiwan.

Table. 2.5-1 Number of auction items of Yahoo and eBay

Date Yahoo! Auction eBay

2003/11/11 3153801 189544

2004/4/4 6885159 456373


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2.6. The operation of online auction Online auction site is a website that allows sellers to list items for sale and

buyers to bid on them in a single-price, sequential auction format. Its only content is

the forms used to list, bid on, and search auctions (and support services like user

feedback and dispute resolution). It owns no merchandise and processes no payments.

Its income comes from the fees that it charges sellers to list items and the

commissions it charges sellers after successful auctionsthose that result in a sale.

A user choosing to bid on an item enters a maximum bid amount. That amount

must equal or exceed the minimum bid, which equals the opening bid or the current

high bid plus an increment. Clearly, the minimum bid grows as the amount of the high

bid grows. After the user verifies the bid with his or her user name and password,

eBay determines the high bidder and computes the new high bid.

After the trade complete, the seller can feedback a score to buyer and the buyer

can feedback a score to seller too. The score might be positive (+1), normal (+0) or

negative (-1). And this feedback score will be recorded forever. Every user on online

auction can query other users feedback history. That is, if a seller hustles his buyer

and really makes his buyer angry. A negative score will be given by the buyer and

other user can find out it.

To illustrate the operation of online auction, an example transaction on

Yahoo!Auction Taiwan is demonstrated here:

1. User choose the merchandise he want to bid.


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2. If the price is ok, buyer can set his bid price.

3. If the buyer win the auction finally, he will receive a notify mail.

4. Seller will received a notify mail too.


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5. The buyer and the seller start their trade. When the trade is complete, they can

give each other a score.


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3. Research methodology

3.1. Research framework The research framework is showed in Fig. 3.1-1. According to this framework,

sellers good reputation, sellers bad reputation, picture quality will affect the number

of bids. Sellers good reputation, sellers bad reputation, picture quality and number of

bids will affect auction performance.

Sellers good reputation

Number of bids

Auction performance

Sellers bad reputation

Picture quality

H1

H2

H3

H4

H5

H6

H7

Fig. 3.1-1 Research Framework

3.2. Hypothesis summary Hypothesizes in this research are listed in Table. 3.2-1. The first hypothesis

supposes that higher number of bids should result in a higher auction performance.

The second hypothesis supposes that finer picture should result in a higher number of

bids. The third hypothesis supposes that finer picture should result in a higher auction

performance. The fourth hypothesis supposes that higher good reputation should

result in a higher number of bids. The fifth hypothesis supposes that higher good

reputation should result in a higher auction performance. The sixth hypothesis


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supposes that lower bad reputation should result in a higher number of bids. The

seventh hypothesis supposes that lower bad reputation should result in a higher

auction performance.

Table. 3.2-1 Hypothesis list

Hypothesis Description

H1 A higher number of bids should result in a higher auction performance.

H2 A finer picture should result in a higher number of bids.

H3 A finer picture should result in a higher auction performance.

H4 A higher good reputation should result in a higher number of bids.

H5 A higher good reputation should result in a higher auction performance.

H6 A lower bad reputation should result in a higher number of bids.

H7 A lower bad reputation should result in a higher auction performance.

3.3. Variables and hypothesis 3.3.1. Number of Bid

This is a measure of the extent to which the auction is hot or cold for no apparent

reason. Clearly, a hot auction should have a higher probability of success and a higher

closing price. From a rational, economic-theory perspective, this could be the result of

a random process of people experiencing a demand for the piece at a particular time.

In this case, the standard auction theory would apply: a higher number of bidders

increases the closing price, which increases the probability of success (Hansen, 1985;

Vincent, 1995). The number of bids, then, is positively associated with auction

success and closing price in that as the number of bids increases, there is more

information available to each bidder (Wilcox, 2000).

An unusually high number of bids also results in more participation, which

produces more information to bidders (Wilcox, 2000). According to Vincent (1995), a

sellers revenues are enhanced when as much information as possible about the value

of goods is provided to the bidders. In addition, bidders can again get caught up in the

bidding, causing an upward cascade, by bidding at higher levels, which would serve


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to increase the chance of success and raise the closing price. Therefore,

H1: All else equal, a higher number of bids should result in a higher auction

performance.

3.3.2. Picture Quality

Seller can put some picture for his merchandise. A good picture will help buyer

to determine if he will bid this merchandise. Finer and clear pictures will improve

buyers understanding on the merchandise. Buyers in online auction market are

always looking for merchandises they need. When they found it, a finer picture is the

commit of the quality of the merchandise. A good picture makes them comfortable

on auction. That is, higher picture quality higher the number of bid. And buyer

would like to pay more because they had seeing the merchandise. Therefore, a four

point scale was designed to measure the merchandises picture quality:

1. No Picture: there are not any pictures for the auction or the picture for the

auction is not the picture of merchandise.

2. Blurry: the picture is blurry.

3. Norma: the picture is clear, but not very complete. E.g. a picture of front,

but lack of back and side picture.

4. Great: With more than 3 clear pictures.

H2: All else equal, a finer picture should result in a higher number of bids.

H3: All else equal, a finer picture should result in a higher auction performance.

3.3.3. Sellers Reputation

Research on brands has indicated that a brand name is an extrinsic cue to quality

in that it provides consumers with a wealth of information about the product (Jacoby

et al., 1978). According to signaling theory (Spence, 1973), in situations where there


17

is information asymmetry (the level of quality is known by the seller but not by the

buyer), the brand name, or reputation of the product/company, can function as a

signal of quality. Reputable brands with have much to lose if they do not deliver high

quality, including loss of investments in reputation (brand equity) and future profits.

Consumers can punish companies by withholding future purchases, leading to the loss of past investments to build brand equity or reputation (Erdem & Swait,

1998). The greater the effort and dollar amount spent to build a brand reputation, the

more credible the signal is (Ippolito, 1990). Therefore, consumers should believe a

reputable brands signals of quality are credible given the negative consequences

associated with false claims (Tirole, 1988).

One distinct feature of on-line auctions is that the buyer and seller do not meet

face to face. Thus, there are often questions relating to trust because the partners lack

information as to the competence and integrity of the other. Brinkman and Siefert

(2001) found that trust was necessary for sellers to participate successfully in an

on-line trading community (eBay). They also found that feedback mechanism on

eBay is an essential part of the system and reduces risks associated with transactions.

Therefore, a seller with a more positive reputation (a higher number of positive

feedbacks) should fare better than a seller with a lesser reputation. In this regard,

seller reputation could function like a brand name, signaling quality and

trustworthiness to the buyer.

Based on the online transaction data, every users feedback history is available

for everybody. Sellers reputation is measured by these three variables:

Sellers number of good feedback,

Sellers number of normal feedback,

Sellers number of bad feedback,

But the number of feedbacks is case by case. For example, there are 82% users

with less than 100 good feedbacks, 16 % users with good feedbacks between

100~1000, and 2% user with more than 1000 good feedbacks in the database of


18

Yahoo!Auction Taiwan in 2003/09. There are some users has extremely high

feedback. Buyer might think that 300 and 2000 are all trustable. But the number

2000 is too large and will impact analyzing result unfairly. So this research

conducted a pilot survey is applied.

A telephone survey is applied on a convenient sample. There are total 122

samples and only 101 of them are valid.

Table. 3.3-1 result of pilot survey.

Question Statistic

Q1 I will set my mind at ease if the seller's number of good feedback is higher than: Mean: 120.8218

Q2 I would like to join the auction and bid for the merchandise, but still concern about seller's trust if the seller's number of good feedback is higher than:

Mean: 28.87129

Q3 It is totally unacceptable if seller's number of good feedback is lower than: Mean: 6.168317

Q4 Seller's number of good feedback doesn't affect my decision Count: 5

Q5 I will set my mind at ease if the seller's number of normal feedback is higher than: Mean: 0.693069

Q6 I would like to join the auction and bid for the merchandise, but still concern about seller's trust if the seller's number of normal feedback is higher than:

Mean: 0.693069

Q7 It is totally unacceptable if seller's number of normal feedback is lower than: Mean: 0.742574

Q8 Seller's number of normal feedback doesn't affect my decision Count: 76

Q9 I will set my mind at ease if the seller's number of bad feedback is lower than: Mean: 1.60396

Q10 I would like to join the auction and bid for the merchandise, but still concern about seller's trust if the seller's number of bad feedback is lower than:

Mean: 2.485149

Q11 It is totally unacceptable if seller's number of bad feedback is higher than: Mean: 8.554455

Q12 Seller's number of bad feedback doesn't affect my decision Count: 0


19

It is obvious; most buyers dont treat number of normal feedback an important

factor. So the sellers reputation only measured by good and bad feedback and is

scaled as four point scale.

A. Sellers good reputation:

(1): number of good feedbacks 6 (2): 6 < number of good feedbacks between 28 (3): 28 < number of good feedbacks between 128 (4): number of good feedbacks 128

H4: All else equal, a higher good reputation should result in a higher number of

bids.

H5: All else equal, a higher good reputation should result in a higher auction

performance.

B. Sellers bad reputation:

(1): number of bad feedback 1 (2): 1 < number of bad feedbacks between 2 (3): 2 < number of bad feedbacks between 8 (4): number of bad feedbacks > 8

H6: All else equal, a lower bad reputation should result in a higher number of

bids.

H7: All else equal, a lower bad reputation should result in a higher auction

performance.


20

3.4. Control variables

3.4.1. Status

When buyer determines to buy a good, the price is important factor which impact

buyers decision. Absolutely, price of brand new merchandise is higher than used

merchandise. Also, used merchandise without flaws can sell higher than flawed one.

Finally, damaged merchandise has the lowest price. Thus, a four point scale to

measure the merchandises status:

Brand new (4): the merchandise is brand new.

Used (3): the merchandise is used and works normally.

Flaw (2): the merchandise exist some flaws, but it still work.

Damaged (1): the merchandise cant work.

And it is obviously that a good with finer status will sell higher, therefore,

All else equal, a finer status should result in a higher auction performance.

3.4.2. Direct buy price

Seller can set a price, called direct buy price. If any ones bid great or equal

than this price, this auction is end and the winner appears. Based on the empirical

observation, this factor may affects the final price, thus, the measure of direct buy

price must be implemented.

3.4.3. Reference Price

Reference price, which is based in part on the past pricing activity of a product,

is stored in a consumers memory and serves as a point of comparison for future

purchases. The unit is NT$.


21

3.5. Dependent variable

There are two dependent variables: final closing price and auction performance.

3.5.1. Final Closing Price

Final Closing price is the final price of auction. If an auction has winner, it has a

Final Closing price. Otherwise, the Final Closing price will be set to NT$ 0. The

unit is NT$.

3.5.2. Auction Performance

A reference price is a very important index in auction. It helps seller to measure

what they gained or loosed. For example, a seller wants to sell merchandise which its

reference price is NT$10000. Finally, seller completes auction, and sell it for

NT$5000. Seller might think that he loosed some benefit in this auction. But if the

final closing price is NT$11000 instead, seller might think that he gained more

benefit.

Thus, a metric scale for auction was developed, which called Auction

Performance. It means the performance of this auction.

For Example, if someone starts an auction, and sells merchandise which its

reference price is NT$ 10,000. Finally, he sells it via auction with Final Closing

price NT$ 8,000, and then this auctions performance is

8000/10000 = 0.8

If merchandises reference price is NT$ 100, and final Closing price is NT$ 95.

Then auction performance = 95/100 = 0.95

3.6. Data collection method Yahoo and eBay maintain a completed auction record after the auction ends and

provide an auction title search utility for completed auctions that allows a user to

enter keywords and to view auctions for which the title included the desired

keywords.


22

There are many sampling methods utilized in statistic analysis. However, it is

very difficult to collect representative data from online auction. Data in online

auction site is hierarchical. There are many reason for such difficulty:

1. The online transaction data is auction market providers confidential data

(eBay, Yahoo.), it is not public data.

2. There are too many auction items in online auction sites, that is , a

proper sampling method must deal with such complexity.

3. There are too many categories in online auction sites. Stratify sampling

is economically infeasible because there are more than ten thousand

categories in it.

3.6.1. Procedure and algorithm

To deal with these difficulties, a collection procedure was developed in this

research. There are 3 functions in this data collection algorithm. Functions of this

proposed data-collection algorithm are defined first in the following section:

Function: C(Cr)

C(Cr) is the total number of auction items contains in category Cr,

C(Cr) is a recursive function which its definition listed below:

Suppose category Cr has sub category C1, C2, , Cn

C(Cr) = C(C1) + C(C2) + + C(Cn)

Equation 3.6-1

The calculation of function C(Cr) can be demonstrated by the example shown in Fig. 3.6-1.

There are two category at first level. And them, there are Category 11 and Category 12 in Category 1; there are Category 21, Category 22, and Category 23 in Category 2. The detail computation and result of C(Cr) is following:


23

C(Category 11) = 2 C(Category 12) = 3 C(Category 21) = 1 C(Category 22) = 4 C(Category 23) = 2 C(Category 1)

= C(Category11)+C(Category12) = 2 + 3 = 5

C(Category 2) = C(Category21)+C(Category22)+C(Category23) = 1 + 4 + 2 = 7

C(Category Root) = C(Category 1) + C(Category 2) = C(Category11)+C(Category12)+C(Category21)+C(Category22)+C(Category23) = 2 + 3 + 1 + 4 + 2 = 12

Auction Category Auction Item

Fig. 3.6-1 Example of tree structure of online auction site.

Function: CuC(Cx)

CuC(Cx) is the cumulative number of auction items

Suppose category Cr has sub category C1, C2, , Cx, , Cn

Category Root

Category 1

Category 2

Category 11

Category 12

Category 21

Category 22

Category 23

Item 111

Item 112

Item 121

Item 122

Item 123

Item 211

Item 221

Item 222

Item 223

Item 224

Item 231

Item 232


24

Cx is sub category of Cr, and C1, C2, , Cx, , Cn is sorting alphabetically.

CuC(Cx) = C(C1) + C(C2) + + C(Cx)

Equation 3.6-2

The computation and the result of CuC(Cx) for the example shown in Fig. 3.1-1 can be demonstrated as follows.

CuC(Category 22) = C(Category 21) + C(Category(22) = 1 + 4 = 5 CuC(Category 2) = C(Category 1) + C(Category 2) = 5 + 7 = 12 Function: CuB(Cx)

CuB(Cx) is the sum of all its sibling auction items.

Suppose category Cr has sub category C1, C2, , Cx, , Cn

CuB(Cx) = C(C1) + C(C2) + + C(Cn)

Equation 3.6-3

The computation of CuB(Cx) for example shown in Fig. 3.1-1 is following:

CuB(Category 22) = C(Category 21) + C(Category 22) + C(Category 23) = 1 + 4 + 2 = 7

The detail steps of data collection algorithm proposed here are:

1. Select root category. Cr, Sample Size : = 0

2. Generate a random number (Rnd) between 1 ~ C(Cr)

Rnd := random number between 1 ~ C(Cr)

3. Find all sub categories: C1, C2, Cx-1, Cx, , Cn

Because of CuC(Cx-1) < Rnd CuC(Cx) Loop X from 1 to n, until finding Cx where CuC(Cx-1) < Rnd CuC(Cx)

4. Select category Cx

5. If Cx still contains sub categories, goto Step 2;

else go to Step 6

6. Generate a random number ( ) between 1 ~ C(Cx):


25

)(~ 1between number random : CxC= 7. Find _item in Cx 8. If _item already stored in database, go to step 10

Else go to step 9

9. Get the reference price of _item from the price list of its vendor. If success, add to database and the final closing price will be collected after the

auction is close, else skip this item. (The final closing price will be recorded

after the auction is end)

10. Sample Size := Sample Size + 1.

If Sample Size < goal, go to step 1;

Else stop.

3.6.2. Proving

The procedure is a fair method because of the probability of sampling each item

is totally equal to random sampling. And it has resolved the problem of how to

sample in auction site. The proving process is shown below:

1. The probability of sampling Cx

Suppose category Cr has sub category C1, C2, , Cn

P(Cx , Cr) is the probability of sampling sub category Cx, in Cr

If CuC(Cx-1) < Rnd CuC(Cx) then Cx will be sampled. And Rnd is a number between 1 ~ C(Cr).

Then

P(Cx , Cr)

= ( CuC(Cx) - CuC(Cx-1) ) / C(Cr)

= )(

)()( 1r

xx

CCCCuCCCuC

= )(

)(...)()(())(...)()(( 12121r

xx

CCCCCCCCCCCCCC ++++++

= )()(

r

x

CCCC

Equation. 3.6-1


26

2. The sampling method is equivalent to random sampling

Suppose there are N auction items in auction site. And are labeled I1, I2, . , IX, , IN Auction market constructing a tree structure, that is, in online auction market, Thus, an auction item IX must belong to some category CRn. CRn must belong to its parent category CRn-1. CRn-1 must belong to its parent category CRn-2. CRn-2 must belong to its parent category CRn-3. CR3 must belong to its parent category CR2. CR2 must belong to its parent category CR1. CR1 is the root of all categories (root of tree) Then Based on Equation. 3.6-1, the result is: P(CR2 , CR1) = C(CR2) / C(CR1) P(CR3 , CR2) = C(CR3) / C(CR2) P(CR4 , CR3) = C(CR4) / C(CR3) P(CRn-1 , CRn-2) = C(CRn-1) / C(CRn-2) P(CRn , CRn-1) = C(CRn) / C(CRn-1) Because there are C(CRn) items located in category CRn The probability of sampling IX in category CRn is 1/C(CRn) The probability of sampling IX in category CR1 is:

)(1)(..........)()()( 1,3,42,31,2

RnRnRnRRRRRR

CCCCPCCPCCPCCP

)(1

)()(

)()(..........

)()(

)()(

)()(

12

1

3

4

2

3

1

2

RnRn

Rn

Rn

Rn

R

R

R

R

R

R

CCCCCC

CCCC

CCCC

CCCC

CCCC =

)(1

1RCC=

N1=

The probability of sampling IX in all items is 1/N This probability is equivalent to that of random sampling

The data collection action was started from 2003/10/01 and end up at 2004/04/01.

There are 50 items to be collected each week, and those items whose final closing

price are not appear are not included. There are total 1032 valid data for analysis.


27

4. Data Analysis

4.1. Status and auction performance One of the most important factors most buyers concern is the status of

merchandise. Brand new merchandise are assumed be in greater shape than old ones,

and should seller higher than old one.

Table. 4.1-1 ANOVA result of status and auction performance

Dependent Variable: auction performance

Source Type III Sum of

Squares df Mean Square F Sig.

Corrected Model 4.180(a) 3 1.393 42.890 .000 Intercept 9.074 1 9.074 279.325 .000 Status 4.180 3 1.393 42.890 .000 Error 33.394 1028 .032 Total 366.223 1032 Corrected Total 37.574 1031

a R Squared = .111 (Adjusted R Squared = .109)

Also, the status of used goods is important for buyers to consider how much to

bid.


28

Means of auction performance

Status of Merchandise

4.003.002.001.00

Estim

ated M

argina

l Mea

ns.7

.6

.5

.4

.3

.2

Fig. 4.1-1 plot of status and auction performance

As shown in ANOVA, the relationship between status and auction performance is

significant (see Table. 4.1-1 and Fig. 4.1-1). Good status makes higher performance.

Therefore, the rest of this data analysis shows two parts: one for all products, in

auction market, and the other for only brand new merchandises. Considering the

auction performance, status should be involved.

Table. 4.1-2 the average auction performance of each status

Status Mean Std. Deviation N

1: Damaged .254 .104 32: Flaw .441 .184 1263: Used .484 .164 1464: Brand New .601 .183 757Total .564 .191 1032

Table. 4.1-1 shows as the status is higher, the mean of auction performance is

higher too. This research is aimed to investigate how information affects bidding


29

performance. To eliminate the noise comes from status; this study use status as

control variable. The original auction performance is measured as:

PriceReferencePrice Closing Finale'PerformancAuction =

After the status is considered as control variable, the auction performance is

modified as the equation listed below:

status itson e'PerformancAuction ofMean 1

PriceReferencePrice Closing FinalePerformancAuction =

In order to understand the difference between used merchandise and brand new

merchandise. All analysis is separated into two parts: all data set and brand new data

set.

4.2. Reputation and auction performance 4.2.1. All Data Set

The result of reputation and auction performance is shown in Fig. 4.2-1 and

Table. 4.2-1. There is no evidence to conclude there exist a significant relationship

between good reputation and auction performance. However, Fig. 4.2-1 shows that

the performance reaches the highest point at reputation = 2, then it falls as good

reputation income.

Table. 4.2-1 ANOVA good reputation and auction performance

Dependent Variable: Auction Performance


Squares df Mean Square F Sig.

Corrected Model .337(a) 3 .112 1.073 .360 Intercept 346.846 1 346.846 3312.363 .000 Good Reputation .337 3 .112 1.073 .360 Error 107.645 1028 .105 Total 1138.958 1032 Corrected Total 107.981 1031


30


Means of Auction Performance

Good Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

1.05

1.04

1.03

1.02

1.01

1.00

.99

.98

Fig. 4.2-1 plot of good reputation and auction performance

To find out the strange result from Fig. 4.2-1, the bad reputation is also taken into

consideration. This strange result is caused by bad reputation. Bad reputation will

cause bad auction performance (show at Fig. 4.2-2 and Table. 4.2-2).

Table. 4.2-2 ANOVA bad reputation and auction performance



Squares df Mean Square F Sig. Corrected Model .832(a) 3 .277 2.661 .047 Intercept 39.407 1 39.407 378.077 .000 Bad reputation .832 3 .277 2.661 .047 Error 107.149 1028 .104

Total 1138.958 1032 Corrected Total 107.981 1031



31


Bad Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns1.1

1.0

.9

.8

.7

Fig. 4.2-2 plot of bad reputation and auction performance

But bad reputation is also positive factor of number of good feedback (Table.

4.2-3 and Fig. 4.2-3). That is, seller with more good feedback also has more bad

feedback for the most part. It is shown in the pilot test that, buyers cant accept too

many risk (too many bad feedback). Buyers will set their mind at ease when sellers

good feedback is higher than 128, but they only feel easy when sellers bad feedback

is lower than 1. Therefore, even a seller has very huge good feedback, if there exist

some bad feedbacks; they think the seller is risky.

Table. 4.2-3 and Fig. 4.2-3 illustrate that a seller with many good feedbacks may

has many bad feedbacks. Thats why the good reputation is a negative factor of

auction performance.


32

Table. 4.2-3 ANOVA bad reputation and good reputation

Dependent Variable: Good Reputation


Squares df Mean Square F Sig. Corrected Model 23039564.373(a) 3 7679854.791 112.585 .000 Intercept 7608910.154 1 7608910.154 111.545 .000 Bad Reputation 23039564.373 3 7679854.791 112.585 .000 Error 70123893.034 1028 68213.904 Total 144203190.000 1032 Corrected Total 93163457.407 1031


Means of Number of good feedback

Bad Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

800

700

600

500

400

300

200

100

Fig. 4.2-3 plot of bad reputation and good reputation


33

4.2.2. Data Set of Brand New Items The Table. 4.2-4 and Fig. 4.2-4 shown an interest phenomenon, good reputation

is a positive factor of auction performance. This result is very different from 4.2.1.

Table. 4.2-4 ANOVA good reputation and auction performance (only brand new)


Source Type III Sum

of Squares df Mean Square F Sig.

Corrected Model .489(a) 3 .163 2.051 .109 Intercept 70.695 1 70.695 888.976 .000 Good Reputation .489 3 .163 2.051 .109

Error 11.929 150 .080 Total 254.473 154 Corrected Total 12.418 153



Good Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

1.4

1.3

1.2

1.1

Fig. 4.2-4 plot of good reputation and auction performance (only brand new)

But another strange phenomenon is the bad reputation also performs positive

factor. This is also different from 4.2.1 and amazing. (Table. 4.2-5 and Fig. 4.2-5)


34

Another interesting phenomenon was founded at Fig. 4.2-5, these sellers with

bad reputation (4) are all disappear. In other words, sellers with more than 8 negative

feedbacks are all disappear. Brinkman and Siefert (2001) had mentioned. A loss of

trustworthiness (negative feedbacks) reduces both the buyer and sellers chances of

participating successfully in the auction community.

Table. 4.2-5 ANOVA bad reputation and auction performance (only brand new)


Source Type III Sum


Corrected Model .870(a) 2 .435 5.691 .004 Intercept 206.483 1 206.483 2700.025 .000 Bad Reputation .870 2 .435 5.691 .004 Error 11.548 151 .076 Total 254.473 154 Corrected Total 12.418 153



Bad Reputation

3.002.001.00

Estim

ated M

argina

l Mea

ns

1.4

1.3

1.2

1.1

Fig. 4.2-5 plot of bad reputation and auction performance (only brand new)

Because of bad reputation is affected by good reputation. And when selling


35

brand new goods, good reputation is more important than bad reputation.

Table. 4.2-6 ANOVA good reputation and bad reputation (only brand new)

Dependent Variable: Bad Reputation

Source Type III Sum


Corrected Model 28.665(a) 3 9.555 15.537 .000 Intercept 101.015 1 101.015 164.261 .000 Good Reputation 28.665 3 9.555 15.537 .000

Error 92.244 150 .615 Total 630.000 154 Corrected Total 120.909 153


Means of Bad Reputation

Good Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

2.4

2.2

2.0

1.8

1.6

1.4

1.2

1.0

.8

Fig. 4.2-6 plot of god reputation and bad reputation (only brand new)


36

4.3. Picture quality and auction performance 4.3.1. All Data Set

A good picture quality should attract buyers, and the buyers should get higher

final closing price. But the result is adverse (Table. 4.3-1 and Fig. 4.3-1).

Table. 4.3-1 ANOVA picture quality and auction performance


Source Type III Sum


Corrected Model 5.589(a) 3 1.863 18.703 .000 Intercept 165.338 1 165.338 1659.951 .000 Picture Quality 5.589 3 1.863 18.703 .000 Error 102.393 1028 .100 Total 1138.958 1032 Corrected Total 107.981 1031



Picture Quality

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

1.1

1.0

.9

Fig. 4.3-1 plot of picture quality and auction performance

This strange result is caused by other noise. The Table. 4.3-2 and Fig. 4.3-2 is

one of the noises: good reputation. Seller with higher good reputation also prefers


37

to provide good picture for his merchandise.

Table. 4.3-2 ANOVA good reputation and picture quality

Dependent Variable: Picture Quality

Source Type III Sum



Error 1934.706 1028 1.882 Total 10227.000 1032 Corrected Total 1959.325 1031


Means of Picture Quality

Good Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

3.0

2.8

2.6

2.4

2.2

Fig. 4.3-2 plot of good reputation and picture quality

And seller with more bad reputation seems to provide worse picture quality

(Table. 4.3-3 and Fig. 4.3-3).


38

Table. 4.3-3 ANOVA bad reputation and picture quality


Source Type III Sum


Corrected Model 12.423(a) 3 4.141 2.187 .088 Intercept 306.624 1 306.624 161.903 .000 Bad Reputation 12.423 3 4.141 2.187 .088 Error 1946.901 1028 1.894 Total 10227.000 1032 Corrected Total 1959.325 1031



Bad Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

3.0

2.8

2.6

2.4

2.2

2.0

1.8

Fig. 4.3-3 plot of bad reputation and picture quality


39

4.3.2. Data Set of Brand New Items Table. 4.3-4 ANOVA picture quality and auction performance (only brand new)


Source Type III Sum


Corrected Model .788(a) 3 .263 3.388 .020 Intercept 58.084 1 58.084 749.152 .000 Picture Quality .788 3 .263 3.388 .020 Error 11.630 150 .078 Total 254.473 154 Corrected Total 12.418 153



Picture Quality

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

1.4

1.3

1.2

1.1

Fig. 4.3-4 plot of picture quality and auction performance (only brand new)

The Table. 4.3-4 and Fig. 4.3-4 shows that if a seller is selling brand new

merchandise, the impact of picture quality is weaker.


40

Table. 4.3-5 ANOVA good reputation and picture quality (only brand new)


Source Type III Sum






Good Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

2.8

2.6

2.4

2.2

2.0

1.8

1.6

1.4

Fig. 4.3-5 plot of good reputation and picture quality (only brand new)

But what is interesting? Seller with good reputation doesnt prefer to provide

good picture quality. A general thinking is good picture for brand new goods is not

important. (Table. 4.3-5 and Fig. 4.3-5)


41

Table. 4.3-6 ANOVA bad reputation and picture quality (only brand new)


Source Type III Sum


Corrected Model 12.627(a) 2 6.313 4.290 .015 Intercept 479.489 1 479.489 325.829 .000 Bad Reputation 12.627 2 6.313 4.290 .015 Error 222.211 151 1.472 Total 831.000 154 Corrected Total 234.838 153



Bad Reputation

3.002.001.00

Estim

ated M

argina

l Mea

ns

2.4

2.2

2.0

1.8

1.6

1.4

Fig. 4.3-6 plot of bad reputation and picture quality (only brand new)

And these sellers with bad reputation still dont want to provide good picture

quality. (Table. 4.3-6 and Fig. 4.3-6)


42

4.4. Number of bids and auction performance 4.4.1. All Data Set

More number of bids means more people want to bid this merchandise. But is it

an important factor of auction performance?

Table. 4.4-1 ANOVA number of bids and auction performance

Dependent Variable: Auction performance

Source Type III Sum


Corrected Model 9.838(a) 78 .126 1.225 .097 Intercept 224.455 1 224.455 2179.510 .000 Number of Bids 9.838 78 .126 1.225 .097 Error 98.144 953 .103 Total 1138.958 1032 Corrected Total 107.981 1031



Number of Bids

10584

7267

6256

5248

4440

3632

2824

2016

128

40

Estim

ated M

argina

l Mea

ns

2.0

1.8

1.6

1.4

1.2

1.0

.8

.6

.4

Fig. 4.4-1 plot of number of bids and auction performance


43

The result (Table. 4.4-1 and Fig. 4.4-1) is insignificantly and we cant find out

any evidence whether number of bids is helpful for final closing price. But in fact,

the effect of number of bids is affected buy whether there is a direct-buy price

existed in this auction. So the analysis was divided two groups: with direct-buy

price and without direct-buy price:

Table. 4.4-2 ANOVA number of bids and auction performance (without direct-buy price)


Source Type III Sum


Corrected Model 8.699(a) 67 .130 1.355 .045 Intercept 161.164 1 161.164 1681.873 .000 Number of bids 8.699 67 .130 1.355 .045 Error 32.388 338 .096 Total 382.777 406 Corrected Total 41.087 405



Number of Bids (only cases without direct-buy price)

8672665854474339353126211713951

Estim

ated M

argina

l Mea

ns

2.0

1.8

1.6

1.4

1.2

1.0

.8

.6

.4

Fig. 4.4-2 plot of number of bids and auction performance (without direct buy price)


44

Table. 4.4-3 ANOVA number of bids and auction performance (with direct-buy price)


Source Type III Sum


Corrected Model 6.181(a) 62 .100 .999 .482 Intercept 124.140 1 124.140 1243.617 .000 Number of bids 6.181 62 .100 .999 .482 Error 56.200 563 .100 Total 756.181 626 Corrected Total 62.381 625



Number of Bids (only cases with direct-buy price)

84655750464236322824201612840

Estim

ated M

argina

l Mea

ns

1.6

1.4

1.2

1.0

.8

.6

.4

Fig. 4.4-3 plot of number of bids and auction performance (with direct-buy price)

In Table. 4.4-2 and Fig. 4.4-2, it is obvious that number of bids has positive

effect to auction performance if there is not any direct-buy price. And Table. 4.4-3

and Fig. 4.4-3 shows the situation if there exists a direct-buy price.


45

4.4.2. Data Set of Brand New Items

Table. 4.4-4 ANOVA number of bids and auction performance (only brand new)


Source Type III Sum





Number of bids

734744403330282218161397531

Estim

ated M

argina

l Mea

ns

1.8

1.6

1.4

1.2

1.0

.8

.6

.4

.2

Fig. 4.4-4 plot of number of bids and auction performance (only brand new)

It is also insignificant when the data set is reduced. Number of bids is not a good

indicator of auction performance with the case selling brand new merchandises.

(Table. 4.4-4 and Fig. 4.4-4)


46

Table. 4.4-5 ANOVA number of bids and auction performance (brand new and without direct-buy

price)


Source Type III Sum





Number of Bids (without direct-buy price)

59464442403332221817169765421

Estim

ated M

argina

l Mea

ns

1.8

1.6

1.4

1.2

1.0

.8

.6

.4

.2

Fig. 4.4-5 plot of number of bids and auction performance (brand new and without direct-buy price)

Also, the relationship between number of bids and auction performance is

insignificant with the case selling brand new merchandises without direct-buy price.

(Table. 4.4-5 and Fig. 4.4-5)


47

Table. 4.4-6 ANOVA number of bids and auction performance (brand new and with direct-buy price)


Source Type III Sum


Corrected Model .935(a) 21 .045 .557 .936 Intercept 46.419 1 46.419 580.005 .000 Number of Bids .935 21 .045 .557 .936 Error 7.043 88 .080 Total 184.184 110 Corrected Total 7.978 109

a R Squared = .117 (Adjusted R Squared = -.093)


Number of Bids (with direct-buy price)

733529271916138531

Estim

ated M

argina

l Mea

ns

1.8

1.6

1.4

1.2

1.0

.8

Fig. 4.4-6 plot of number of bids and auction performance (brand new and with direct-buy price)

Number of bids is still insignificant when selling brand new merchandises with

direct-buy price. (Table. 4.4-6 and Fig. 4.4-6)


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4.5. Reputation and number of bids 4.5.1. All Data Set Table. 4.5-1 ANOVA bad reputation and number of bids

Dependent Variable: Number of Bids

Source Type III Sum


Corrected Model 43435.368(a) 7 6205.053 21.970 .000 Intercept 8439.346 1 8439.346 29.881 .000 Is has direct-buy price?

2132.552 1 2132.552 7.551 .006

Bad Reputation 12849.874 3 4283.291 15.166 .000 Is has direct-buy price * Bad Reputation

8653.573 3 2884.524 10.213 .000



Means of Number of Bids

Bad Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

40

30

20

10

0

is has direct buy

.00

1.00

Fig. 4.5-1 plot of bad reputation and number of bids


49

The impact of bad reputation is more significant when there is a direct-buy price.

(Table. 4.5-1 and Fig. 4.5-1)

Table. 4.5-2 good reputation and number of bids


Source Type III Sum


Corrected Model 23870.449(a) 7 3410.064 11.309 .000 Intercept 50216.911 1 50216.911 166.537 .000 Good Reputation 4402.691 3 1467.564 4.867 .002 IS_DBUY 7764.440 1 7764.440 25.750 .000 Good Reputation * IS_DBUY

193.141 3 64.380 .214 .887


a R Squared = .072 (Adjusted R Squared = .065) table 4.9


Good Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

30

20

10

0

is has direct buy

.00

1.00

Fig. 4.5-2 plot of good reputation and number of bids

Good reputation is a positive factor of number of bids obviously. And the Fig.


50

4.5-2 shows a phenomenon: without direct-buy price, seller can attract more

buyers attention.


Table. 4.5-3 ANOVA good reputation and number of bids (only brand new)


Source Type III Sum


Corrected Model 4336.012(a) 7 619.430 3.351 .002 Intercept 4729.412 1 4729.412 25.585 .000 Good Reputation 3677.620 3 1225.873 6.632 .000 is has direct buy price?

412.644 1 412.644 2.232 .137

Good Reputation * is has direct buy price

1114.424 3 371.475 2.010 .115




51


Good Reputation

4.003.002.001.00

Estim

ated M

argina

l Mea

ns30

20

10

0

is has direct buy

.00

1.00

Fig. 4.5-3 plot of good reputation and number of bids (only brand new)

When selling brand new goods, both good reputation and bad reputation are both

insignificant. (Table. 4.5-3 , Table. 4.5-4 , Fig. 4.5-3 and Fig. 4.5-4)

Although the result of ANOVA is significant, the plot seems has no trend visible.

Because of the independent variables in ANOVA analysis is not in numeric order. So

the ANOVA analysis shows a significant result.


52

Table. 4.5-4 ANOVA of bad reputation and number of bids (only brand new)


Source Type III Sum


Corrected Model 6174.537(a) 5 1234.907 7.267 .000 Intercept 12588.551 1 12588.551 74.080 .000 is has direct buy price?

2451.424 1 2451.424 14.426 .000

Bad reputation 5538.086 2 2769.043 16.295 .000 is has direct buy price * Bad reputation

1801.168 2 900.584 5.300 .006




Bad Reputation

3.002.001.00

Estim

ated M

argina

l Mea

ns

40

30

20

10

0

is has direct buy

.00

1.00

Fig. 4.5-4 plot of bad reputation and number of bids (only brand new)


53

4.6. Picture quality and number of bids 4.6.1. All Data Set

Table. 4.6-1 ANOVA picture quality and number of bids

Dependent Variable: Number of bids

Source Type III Sum

of Squares Df Mean Square F Sig.

Corrected Model 7489.895(a) 7 1069.985 3.370 .001 Intercept 16907.848 1 16907.848 53.248 .000 Is metropolis? 886.439 1 886.439 2.792 .095 Picture quality 3564.484 3 1188.161 3.742 .011 Is metropolis * Picture quality 771.763 3 257.254 .810 .488




Picture Quality

4.003.002.001.00

Estim

ated M

argina

l Mea

ns

16

14

12

10

8

6

4

is metropolis

0

1

Fig. 4.6-1 plot of picture quality and number of bids


54

The picture quality is effective if the auction is located in metropolis area. (Fig.

4.6-1)


Table. 4.6-2 ANOVA of picture quality and number of bids (only brand new)


Source Type III Sum


Corrected Model 799.854(a) 7 114.265 .547 .798 Intercept 1421.523 1 1421.523 6.799 .010 Picture quality 633.421 3 211.140 1.010 .390 Is metropolis? .529 1 .529 .003 .960 Picture quality * Is metropolis

241.620 3 80.540 .385 .764


a R Squared = .026 (Adjusted R Squared = -.021)


55


Picture Quality

4.003.002.001.00

Estim

ated M

argina

l Mea

ns14

12

10

8

6

4

2

0

is metropolis

0

1

Fig. 4.6-2 plot of picture quality and number of bids (only brand new)

The picture quality is ineffective if there are only cases of brand new goods.

(Table. 4.6-2 and Fig. 4.6-2)


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4.7. Adjusted model

Sellers good reputation

Number of bids

Auction performance

Sellers bad reputation

Picture quality

positive

positive

positive*2

negativepositive *1

Negative*3

positive

negative

H4

H6

H7

H2

H3

Fig. 4.7-1 adjusted model

Table. 4.7-1 Hypothesis testing result

Hypothesis Description Is support Special Case

H1 A higher number of bids should result in a higher auction performance. Not Positive when there is no direct-buy price.

H2 A finer picture should result in a higher number of bids. Not Positive when it is located at metropolis.

H3 A finer picture should result in a higher auction performance. Not

H4 A higher good reputation should result in a higher number of bids. Yes

H5 A higher good reputation should result in a higher auction performance. Not

H6 A lower bad reputation should result in a higher number of bids. Yes

H7 A lower bad reputation should result in a higher auction performance. Yes

*1: sellers with more negative feedbacks usually have more positive feedbacks.

*2: sellers with more positive feedbacks prefer to provide good picture.

*3: sellers with more negative feedbacks prefer to provide bad picture.


57

The analysis result shows the good reputation, bad reputation and picture quality

are all important factors of number of bids. That is, the merchandise which is sold by

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