Late Bidding, Bidder Categories and Network Externality Effects: A Preliminary Examination of Online Auctions

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1 Late Bidding, Bidder Categories and Network Externality Effects: A Preliminary Examination of Online Auctions Rong-An Shang Ming-Kuan Ling Department of Business Administration, Soochow University, Taipei, Taiwan rashang@mail2.scu.edu.tw mingkuanlin@yahoo.com.tw Abstract Auctions are emerging as an important Internet application for price discovery and trading. The Internet also enables new types of auction formats and creates a venue to observe and study new auction models. In this study we hypothesize that the pattern of online bidding is impacted by network externalities, relative price level, and auction duration. We also hypothesized the context of the auctions and the types of the bidders will affect the impact of these incentives on bidders behavior. Real transaction records of a special on sales on a popular auction site in Hong Kong were collected to test these hypotheses. Preliminary results from our study suggest that the impact of network effects, relative price, and time period will be affected by the context of the auctions and types of bidders. 1. Introduction In the last decade online auctions have assumed increasing importance in business to business and business to consumer commerce. This has been driven by the evolution of the Internet and World Wide Web, which creates inexpensive communications and computing platforms for aggregating buyers and suppliers into electronic markets. By lowering transaction costs through cheaper communications and coordination, the Internet enables the greater use of electronic markets and new types of transactions [15,21]. As electronic markets proliferate some researchers have questioned whether online markets and auctions can replace the role of traditional intermediaries [3,24]. One major function of intermediaries is to gather, organize, and evaluate information, which is dispersed in the society, and based on the collected information to decide the price of the products to clear the market [27,29]. Traditionally, it has been costly and difficult for firms to get sufficient information about customers and competitors to make the pricing decision. In addition, there are significant costs to coordinating pricing decisions within firms [9]. Posted offer prices were used on many retail items due to the high menu costs of varying prices. But the move to selling over the Internet reduces menu costs and also enables the use of dynamic pricing models and bargaining agents for more efficient price discovery [14]. Auctions provide one class of methods for dynamically and socially constructing prices. They rely on the interactions of multiple bidders to determine a price. Traditionally, auctions were seldom used in consumer markets with the exception of expensive collectables due to the high costs of bringing buyers and sellers to a physical venue. However, online auctions dramatically reduce the costs of aggregating buyers and sellers to a virtual setting, enabling the widespread proliferation of auctions in consumer and business settings. Companies such as ebay, Ubid, Priceline, and FreeMarkets have all illustrated their successful use in business to consumer and business to business transactions. As Kambil and van Heck [16] note auctions are being applied more broadly to a range of business problems from resource allocation decisions within firms, to predication markets, and the use of resale markets strategically to drive new product adoption and learn customer preferences. Although auctions are an important Internet application, economics does not have a precise theory of auctions [12]. As managers and firms confront the possibilities of using new auction mechanisms, they have to learn a considerable amount from auction design to deployment strategies to successfully adopt these mechanisms [11,13,16]. Prices on the auction markets are determined by the bidders behavior, which in turn affected by the rules that define the auction. Because Internet enables different auction designs and rules, /04 $17.00 (C) 2004 IEEE 1

2 bidding behaviors on Internet should not be the same within in different online auction markets. Our study will limit itself to study the mechanism of English auction, which widely used in practice. Through understanding bidder behaviors and habits in this context we hope it will help designers improve the deployment of these auctions in practice, and enable bidders to bid more effectively. English auctions on the Internet raise two interesting issues - late bidding (or sniping) and the effects of network externalities. Late bidding means bidders will not bid until the latest few minutes or even seconds to avoid revealing their preference. Under some English auction designs which encourage late bidding, the ending price, which determines the seller s revenue, can diverge from the most efficient clearing price. As most online English auctions reveal the bids or others, we also assume the operation of positive network externalities in the auction process, where the bidder s decision will be influenced by the actions of the others. Thus we expect a bidder s behavior will also be influenced by current price level of the bid and current time in the auction. This study investigates the impacts of the context of the auctions and the types of the bidders on the effects of network externality, relative price level, timing of the bids, and late bidding. Field data collected from Go2HK, a Hong Kong online auction was used to test our research model. The results show that the context of the auctions and types of the bidders would affect the impact of relative price level, network effect, and timing of the bidding, but would not affect the impact of last minute effect. 2. Auctions on the Internet Auctions are processes of formalized dickering. They define formal rules for interaction among multiple bidders and the auctioneer to exchange information and socially construct prices [16]. There are many different auction mechanisms like the English ascending-bid auctions, the Dutch descending-bid auctions, the first-price sealed-bid auctions, the Vickrey second-price sealed-bid auctions and double auctions [8,20]. The Internet enables the construction of new types of auctions and allows their greater proliferation by simplifying and reducing the costs of aggregating buyers and sellers in a virtual market place. Previously physical auctions often required people to synchronously co-locate so they could hear and see each other bidding. Today this is no longer necessary auctions can have asynchronous and non co-located participants. As Kambil and van Heck [16] note the Internet introduces new flexibility in auction design. These include varying: the number of bidders qualified to bid, the number of offerings (multiple or single unit offers), the sequence of product offerings, the combination of items, the product description, the duration and stopping time of the auction, the bid increments, the rating mechanisms of buyers and sellers, the feedback offered bidders and speed of the auction. All of these items can be more varied in Internet auctions and can impact final price outcomes. As the Internet enables new variety in auctions, new tools have emerged to support bidders. For example many online auctions last several days and bidders would have to stay on line a long time to raise their bids. However, the proxy bidding software solves this problem for bidders by providing a software agent to bid automatically on the bidder s behalf. When the current price is below the upper limit price specified by the bidder, the agent will bid automatically on the price mark up by the minimum increment set by the seller [12]. Thus people don t have to stay on line and can bid on any convenient time during the auction period. Such tools facilitate asynchronous auctions and lower the costs of buyers to participate in and use auctions. In this study we limit our study to English auctions of a particular type and focus on late bidding and network externality impacts Late bidding problem Because many consumer auctions on the Internet usually continue for several days, there can be late bidding, or sniping. Ockenfels and Roth [23] observe 308 ebay auctions of computers and antiques with a total of 1339 bidders. They show that, although ebay-auctions usually last one week, many bidders bid in the very last minutes of the auctions. For example, 14 percent of all bidders in this sample submitted their final bids in the last five minutes, 9 percent in the last minute, and more than 2 percent in the last ten seconds before the deadline. Late bidding is problematic for both the bidders and the sellers. Lucking-Reiley [19] shows that the sniping destroys the English auction s attractive feature that bidders have a dominant strategy to bid up to their maximum willingness to pay, making the optimal bidding strategy a complicated guessing game. In addition, there are risks to the buyers and sellers in last-minute bidding. Because the time it takes to place a bid may vary considerably due to network congestion or connection times, last-minute bids have a positive probability of being lost. On the other hand, few people bid in the early stage of the auction and congestion failures in the last minute may cause the ending price be lower than the highest price the bidder is willing to pay. This can create a potential loss for the sellers. Is late bidding a rational strategy? Why is it so prevalent in some online auctions? As we discuss below late bidding appears to be driven by the rules of the auction and how bidders think. Hall [12] argues that traditional auction theory is based on the idea that the players all have cutoff prices in mind before the auction begins. If all bidders are eventually going to enter bids with their true maximums, it just does not matter, whether /04 $17.00 (C) 2004 IEEE 2

3 you bid at the beginning, middle, or end of the auction, or when others bid, has no effect on the outcome. The winner is always the player with the highest cutoff price, and the price is always the runner-up maximum plus the bid increment. However, if there are two bidders with the same highest cutoff price, in some auctions the winner will be the one bid earlier. So, in order to win the auction, the bidders should bid as early as possible and late bidding seems not to be a rational strategy. But in reality the conventional theory misses the mark. As Hall [12] notes there is lot evidence that people learn auctions and adjust their ideas about the value of what is for sale. Bidders learn about the value of the goods by seeing the bids of others. They don t know their cutoff values before an auction begins [20]. Rather than having a good idea of the maximum they are willing to pay, they have a general idea that they want an object, and a firm desire not to pay more than it is worth. They would like to gather information from others about an object s value. Therefore, Hall suggests experts who know the value of the item will not bid until the end of the auction in order not to reveal their private information and adversely raise the price. Similarly, Roth and Ockenfels [26] consider late bidding to be a rational strategy and argue that sniping is used to avoid bidding wars and to protect information. They also notice that in the early Internet auctions, bidders that wanted to bid close to the end of the auction, had to be online at the end of the auction and submit their bid by hand. Fortunately for late bidders, sniping agents like esnipe now allow a bidder not only to submit a proxy bid, but also to do so at the last moment of an auction. A simple Google search shows several free and commission based sniping tools. However, Bapna [4] notes these agents can lower economic efficiency, promote collusive bidding and could ultimately push electronic auctions towards a precipice. Because late bidding has raised some problems in online English auctions there are number of solutions designed to encourage early bidding and restore the mechanism s desirable properties. The first is to offer a short extension period to the auction, where the auction continues after the closing time until there have been no bids arrived for a few minutes [1,22,23]. But this creates the disadvantage of an uncertain closing time for serious bidders. Nevertheless, studies of the ebay auction with a fixed closing time and Amazon auction that permits extensions confirm that the stopping rule can impact late bidding. There is more sniping on ebay by experienced bidders than on Amazon. The second solution is to implement proxy bidding for the auction. The latter strategy serves to Vickrify the auction, making the fixed-length English auction resemble the Vickrey second-price sealed-bid auction [19]. This way not only retains the essence of Vickrey auctions but also eliminates incentives for sniping successfully. However, real behaviors on online auctions are not always simple, nor always rational. Despite possible design alterations to discourage late bidding, not all Internet auction sites provide the mechanisms to solve the problem [12,19], and not every bidder will wait till the last minute. So, why do some bidders choose to bid early? Ariely and Simonson [2] propose a stage model to describe the decisions of the bidders. They argue that decisions of the consumers to entry an auction differ from the decision to buy the product and required less commitment. Besides, bidders with different objectives, information, preferences, risk aversion and experiences may also behave differently on the Internet auctions [2,7,30]. Bapna et al. [5] study bidders strategies in multi-item online auction and the result of the data mining showed there were three types of bidders: evaluators, participators, and opportunists. Evaluators are those who know the true market value of the goods and try and bid that amount early to win the auction. Their bidding higher than the minimum required increment indicates their desire to minimize the uncertainty of being priced-out of the auction, however, they bear the risk of bidding more than required to win the auction. Participators are bidders who follow the bidding closely and place ascending bids, they never bid any higher than the current minimum required bid. Opportunists are driven by thoughts of getting bargains; they usually wait till the last moments and placing the lowest possible bids. The strategy of opportunists causes the problem of late bidding. However, Bapna et al. [5] also introduce the concept of loss of surplus, which is defined as the difference between an individual s bid and the minimum winning bid, to measure the performance of the bidders in the multi-item auctions. The empirical evidence of their study suggested that participators tend to perform better than opportunists, and who in turn perform better than evaluators. Ward and Clark [30] investigate how bidders use proxy bidding on ebay. They find that the ebay auctions won by proxy bids submitted during the first half of the auction result in higher revenues than auctions won by proxy bids submitted during the latter half of the auction, greater revenues are achieved by auctions won by proxy bids submitted during the first half of the auction than by auctions won by minimum increment bids, and finally, auctions won by proxy bids submitted during the last ten minutes of the auction also appear to result in higher revenues than auctions won by minimum increment bids. They also show that there was a significant difference between experience level of early proxy and minimum increment bidders. Relatively high early proxy bids are more likely to have been submitted by inexperienced bidders. These inexperience bidders tend to be more risk averse characterized by the more insurance premium paid by many early proxy bidders. Results of these studies show bidders behaviors are /04 $17.00 (C) 2004 IEEE 3

4 not perfectly rational. They are also related to the prior experience of bidders with auctions. People may bid in the early stage of the auction because they are inexperienced in auctions with a fixed closing time. However, late bidding may not always be the best strategy for bidders in these auctions. Bapna s [5] work on multi-item auctions shows the participators who follow auctions closely and increase bids by the minimum increment often outperform evaluators and opportunists as measured by the loss of surplus Network effects Network externalities occur when the utility of using a technology increases as the network of adopters expands [17,25,28]. Economides [10] distinguishes two types of networks, i.e. One-way networks and Two-way networks. The former occurs when network components come together to from composite goods. Examples of one-way networks include paging networks and radio broadcasting. The later includes telephone systems, fax machines, and , where it is possible to distinguish a direction of flow in a network, and where users on both ends of a connection can share in the benefits. There are also two types of network effects in auctions. The first is related to the auction sites. When there are more bidders at the auction, the competition among bidders is likely to be more intense and drive prices higher. This in turn will attract more sellers to auction their goods on the site, which in turn will increase the diversity of goods offered and attract more buyers to participate. The second externality effect is related to the valuation of the items. The values of the items placed for auction may be private or common. The private value means that bidders each value the item different. In contrast, the common value means that auctioned items hold the same value for all bidders. The value can also be independent or affiliated. Values are independent when bidders have no information regarding the value of the auctioned item to other potential bidders. Values are affiliated if bidders who receive information that indicates their own values for a particular item are likely to be high (or low) have reason to believe that other bidders values are also likely to be high (or low) [18,30]. For affiliated or common value auctions, one bidder s decision should be affected by the other s bids. If there are more buyers bidding for the item, the others will value the item higher and be more willing to bid higher. The network effect can cause a positive feedback effect in some auctions and raise the price. However, when interrelated with late bidding, network effects can also lead to a negative feedback. If few people bid in the early stage of an auction, there may be less people willing to bid afterward. So late bidding can diminish externality effects and result in lower final prices in some auctions. 3. Research Model Based on the analysis of prior literatures we propose the model illustrated in Figure 1, for the number of bids in any period of an English auction with a fixed closing time. As shown in figure 1, numbers of bids in a period should be affected by four factors. First, given network externalities we can expect the numbers of bids in the preceding period will be positively related to the numbers of bids in current period. Second, we hypothesize a price level effect. Lower price goods are likely to be more affordable and attract more bidders. Thus we expect there will be more bids in a time period. Third, given the stopping rule of auctions with a fixed closing time we expect a positive relationship between time period and the numbers of bids in the period. In other words, more people will be willing to bid nearer to the end of auction to minimize revelation of private information. Finally, we hypothesize the maximum number of bids will occur in the last period of the auction. There are two moderate factors in the model. First, as proposed by Bapna et al. [5], we expect bidders with different strategies will behave differently on Internet auctions. Roth & Ockenfels [26] and Ward & Clark [30] also argue that bidders behavior will be affected by their Network effect Numbers of bids in the preceding period Context Price level effect Time period effect Numbers of bids in the period Last minute effect Member type Figure 1: Research model /04 $17.00 (C) 2004 IEEE 4

5 prior experiences on Internet auctions. Thus we expect differences among the experience and types of bidders to moderate the number of bids in any specific period. Second we expect the context of the auction to also moderate the number of bids. For example if there are exclusive auctions to a specific category of participants we expect the bidding behavior to differ across auctions based the context of the auction. 4. Study Setting and Design To test our model we selected Go2HK ( a Hong Kong based auction site for several reasons. First, despite the listing over one hundred auctions in two Chinese search engines, most other auction sites were relatively small. In contrast Go2HK had a large number of buyers and seller so this auction site appeared to be a fairly efficient market place. Second, the site deployed a form of the English auction, a popular Internet auction mechanism that we wished to study. Third, auctions on this site were open-book, where details of the auctions such as the buyer identification, price, and time of each bid were provided. These allowed us to record and study the bidding behaviors participants. Data for this study was collected from a special of auctions on Go2HK. We named this On-Sale Auctions (OSA). Products sold on OSA were newly manufactured goods provided by the company and auctioned under some special rules. First, the bidders are not allowed to use proxy bidding, and there is no reserve price as well. The highest bidder, therefore, is obligated to conclude the transaction, even if the item receives only one bid. Secondly, the OSA began at 6:00:00 p.m. and closed exactly at 0:00:00 a.m., without the extension period. Thirdly, no matter what product was been sold, initial bidders must bid at the starting bid, which is always HK$ 10. Finally, subsequent bidders must increase their bid by a minimum increment. The minimum increment is set by Go2HK based on the reference price for the product that is also published on the web site. No bids more than the minimum increments are allowed and bidders cannot submit bid more than two consecutive bids at time. Go2HK used this (the On-Sale Auctions) to offer buyers special discounts and to attract buyers to participate in the site. Compared to traditional English auctions, the rules of these modified English auctions constrain jumped bidding behaviors and require more bids to raise the price to a specific level. Bidders are nevertheless attracted to these auctions given the opportunity to buy high quality products at discounted prices. This also provided some advantages for our study. Because bidders can neither use proxy bidding nor bid more than the minimum increment, most decisions other timing and bid value are generally constrained. So the bidders behavior is simplified and we can focus on the timing of the bids. There were two types of members in Go2HK, VIP and regular members. The VIP members were super members on Go2HK. They had to honestly provide key personal data, including their name and Hong Kong ID numbers and the means of communication, and had signed application forms and contracts of membership with Go2HK. Every VIP member had an icon of VIP next to his or her name to identify his or her membership. The VIP members were authenticated by Go2HK and had committed to a contractual agreement with the auction. In return they were permitted to participate in the VIP only auctions. The two categories provide us with a way to type bidders and also consider how variations in context (VIP vs general auctions) impact bidding behaviors. A basic question in categorizing online bidders is whether we can capture some consistent traits that would allow us to development a formal typing methodology [6]. The existence of VIP versus regular members provides one possible categorization. In this study we hypothesize that VIP members are more committed to, and experienced with Go2HK auctions than regular members. While the level of experience was not directly measured, we can assume that VIP members were more deliberate than regular ones in joining and participating in the auction. VIP members would have carefully considered the rules, objectives, costs, and benefits of bids on the auction site, before they signed the forms and contracts. In this study we propose that the deliberate level of a bidder can affect his/her behavior in the auction, and this impact can be shown as the moderate effects of the types of members between the four explanatory variables and the numbers of bids in the period, as in the research model. We hypothesize that the VIP-members are more experienced and deliberate in their bidding than regular members. The existence of VIP and regular auctions also enable us to examine if there are any effects related to context, and interactions with different types of bidders. The hypothesize context of the auction could affect bidders behavior and this effect is illustrated as potential moderate effect in our research model. We hypothesized that bidding patterns in the VIP will from that in the general because bidders behaviors are likely to be more varied on the general. This in turn is likely to drive more complicated interactions among bidders and in turn resulted in different behaviors. The OSA took place two times a week, on Tuesdays for VIP members only (but on Wednesday before September, 2002), and on Thursdays for all members (but on Friday before September, 2002). We called the two s VIP and general. The separated for VIPs offered special discount and incentives for those members. Data of the bidding histories of auctions in OSA in about 28 months were collected as the sample in the study, which began in July 1999 and ended /04 $17.00 (C) 2004 IEEE 5

6 Table 1: Types of auction s in the sample VIP General Total Numbers of auctions Number of bids Numbers of bids by VIP members Numbers of bids by regular members Table 2: Types of members in the sample in October However, some of the auctions in this period were multi-itemed, data of this auctions were discarded because bidders behavior in multi-itemed auctions may be different with their behavior in single-itemed ones. Totally, there are data about 224 auctions, 7568 members, and bids in the study. 116 of the 224 auctions were for VIP members only, and 108 auctions were for all members. Table 1 and 2 show the descriptions of the sample in the study. All of the auctions in the OSA continued for six hours. We divided the 6 hours into 36 time periods; each period was 10 minutes equally. Then we counted the numbers of bids in each period to measure the bidders behavior in the auction. Figure 1 shows the research model of this study. 5. Data Analysis VIP members Regular members Total Total In VIP In general 1013 a a : All of the VIP members bid in the general had also bid in the VIP. Multiple regression analyses and 2-way ANCOVA were performed to test the direct and moderate effects in the research model. We used multiple regressions to study bidding patterns in four subsets of the sample separately and to control the moderate effects. These subsets were bids in the VIP, bids in the general, bids of VIP members in the general, and bids of regular members in the general. The dependent variable was the numbers of bids in period i (NOB i ). For the studies of VIP and regular members in the general, NOB i indicated only the numbers of bids belonging to VIP or regular members in a period, respectively. According to our research model, four independent variables were included in the analysis. The first was the numbers of bids in preceding period (NOBPP i ). For the studies of VIP and general, NOBPP i = NOB i-1, however, for the studies of VIP and regular members in the general, NOBPP i equals to the sum of numbers of bids belong to VIP and regular members in the preceding period. Second, price level effect was indicated by the natural logarithm of the relative price differences (LRPD i ). Let P i be the ending price in period i, and Pr be the reference price of the product, which was provided the auction site and shown on the web page. LRPD i was defined as ln((pr-p i-1 )/Pr+1). The logarithmic transformation was used because studies of cognitive psychology show that the relationship between human perception and the difference value is not linear. When the difference is larger, the weighting is smaller. One was added to the relative price difference to avoid taking the log of 0 or negative value. The third independent variable was time period (TP), which equals to i in the ith period. Finally, a dummy variable last period (LP) was added to show if it s the last period. LP equals to 1 when the TP equals to 36, and LP equals to 0 otherwise. 7 auctions in the VIP and 5 auctions in the general were excluded from the sample because the reference prices of the products were not shown on the web page. So the final sample contains 212 auctions. However, the residual analysis of the multiple regressions showed the distributions of residuals were not normal and right skewed. So the dependent variable NOB and the independent variable NOBPP were also logartithmically transformed. Table 3 presents the summarized results of the multiple regression analysis in the four subsets of the sample. The results of our analyses showed that the regressions in the four samples were all significant. Only two effects of the independent variables were not supported in these regressions, the effect of LRPD in VIP and the effect of TP in the sample of regular members in general. Because LRPD is the only independent variable that can attract bidders to bid in the early stage of the auction, this result indicated there would be more serious problem of late bidding for auctions in the VIP. On the other hand, bidders in the general could be motivated by relative price level, even for VIP members in the general. This result also supported the idea that the bidder s behavior will be affected both by their traits and the interactions in the bidding. The insignificant of the effect of TP for regular members in general suggested that regular bidders would not notice the time of the bidding, except for in the last 10 minute. This is consistent with our assumption that the regular members are less deliberate than the VIP ones. Comparing the coefficients of LP in the regression models with the coefficients of other variables shows a tremendous impact of last minute in all of the four samples. Taking the model in VIP for example, /04 $17.00 (C) 2004 IEEE 6

7 Table 3. Results of the multiple regressions VIP General VIP members in general Regular members in general F-value * * * * R N Intercept Beta t-value * * * * NOBPP Beta t-value * * * * LRPD Beta t-value * * * TP Beta t-value * * * LP Beta t-value * * * * * : p<0.001 numbers of bids in the last period would be 1.85 times, which equals to e 0.616, more than the numbers of bids in the other period, all other conditions being equal. The coefficients of the effects of LRPD in all of the three samples of general also suggested a tremendous impact of relative price level for auctions in general. For example, when the relative price difference is equal to 0.2 ((Pr-P i-1 )/Pr = 0.2), the numbers of bids in the general will be 1.42 times, which equals to (0.2+1) 1.92, more than the numbers of bids when the relative price difference is 0, other things being equal. We also conducted stepwise regressions to compare the variance the dependent variables explained by the four explanatory variables. Summaries of these studies were shown in table 4. Most of the variances of the dependent variables explained by the explanatory variables were contributed by NOBPP. This results showed a strong impact of network effects in all of the four samples. Despite the types of the bidders and the context of the auctions, the valuation of a bidder and the incentives to bid should be affected by the action of the others. In the meanwhile, for the auction in the general, relative price differences contributed to the second largest portion of the explained variance. As discussed in the previous paragraph, the results showed a significant difference of the effects of relative price between auctions in the VIP and general s. Finally, although last minute effect could bring huge impact on the numbers of bids, it explained only a minor portion of the total variances because last minute effect occurs only in the last period. A 2-way ANCOVA (ANalysis of COVAriance) was performed to test moderate effects in the research and to compare the four regression models of different samples in the first study. Two categorical variables, context of the auctions and types of the members, were used in the analysis. However, there were no data about the biddings of the regular members in the VIP. Only three combinations of the two categorical variables were in our samples. Although we use 2-way ANCOVA analysis, the interactions effect of the two categorical variables couldn t be tested in the model. The dependent variable NOB and the independent variable NOBPP were also logarithmically transformed as suggested by the regression analysis. Table 5 shows the results of the analysis. The results of ANCOVA showed the interactions effects of LP with context and types of member were both insignificant. So the last minute effect should be very robust across different contexts the bidders. The impact of last period on the numbers of bids would not be affected by the context of the auctions and the types of bidders. Except for that, the other interactions effects in the model Table 4. Summaries of the stepwise regression analyses VIP General VIP members in general Regular members in general Sequences Variable R 2 Change Variable R 2 Change Variable R 2 Change Variable R 2 Change 1 NOBPP NOBPP NOBPP NOBPP TP LRPD LRPD LRPD LP LP TP LP TP LP /04 $17.00 (C) 2004 IEEE 7

8 were all significant. The impact of numbers of bids in the preceding period and time period would be smaller, and the impact of relative price differences would be larger, for the auctions in the general and for the bids of regular members. 6. Conclusions Table 5. Result of the ANCOVA Source F coefficient t-value Corrected Model * Intercept * * Context(=general) * * Member(=regular) * * NOBPP * * LRPD * TP * * LP * * Context * NOBPP * * Context * LRPD * * Context* TP * * Context * LP Member * NOBPP * * Member* LRPD * * Member* TP * * Member * LP N = 11340; R 2 = ; adjusted-r 2 = * : p<0.001 The purpose of this study was to investigate the impacts of the context of the auctions and the types of the bidders on the effects of network externality, relative price level, timing of the bids, and late bidding. Some interesting preliminary findings were obtained from this study. First, the relative price level does not affect the bids in the VIP but affects the bids in the general. Our results suggest that bidders in the VIP are not especially attracted to bid by the low price of the goods been auctioned. Second, behaviors of VIP members in the VIP appear to differ from their bidding behaviors in the general. This result supported the idea that bidder s behavior was not stable independent of the context and would be affected by the interactions with other types of bidders. Third, the context of the auction and types of the bidders show an impact on bidders behaviors. The impact of the numbers of bids in preceding period and the time period would be smaller, and the impact of relative price differences would be larger, for the auctions in the general and for the bids of regular members. Fourth, for these auctions the last minute effect has a huge impact on the numbers of bids. Furthermore, the impact of last minute bidding was stable and without differences across various types of members and different context. Results of this preliminary study suggest that in order to reduce the impact of late bidding and drive a positive network effects, there should be more varied types of bidders in the auctions. This reduces the predictability of the auction game, and can make it harder for participants to find a dominant strategy. While we had the advantage of investigating real auction transactions, there are a number of limitations to this preliminary study. First there are a number of constraints such as fixed timing, fixed increments, and no proxy bidding in these auctions which make the results only generalizable to similar auctions. Furthermore, OSA was a special auction on Go2HK. The purpose of this service was to provide members special discount, but not to raise the price to increase the revenue of sellers. Although we could study bidders behavior in such a simplified environment, the findings of this study still need to be further investigated in a more standardized English auction context. Finally in future studies we hope to do a more distinctive classification of auction participants based on more individual traits. Reference [1] Ariely, D., Ockenfels, A. and Roth, A. E., An Experimental Analysis of Ending Rules in Internet Auctions, Working Paper No , Sloan School of Management, Massachusetts Institute of Technology, July [2] Ariely, D. & Simonson, I., Buying, Bidding, Playing, or Competing? Value Assessment and Decision Dynamics in Online Auctions, Journal of Consumer Psychology, vol.13 no.1&2, 2003, pp [3] Bakos, Y., The Emerging Role of Electronic Marketplaces on the Internet, Communications of the ACM, Vol. 41, No. 8, August 1998, PP [4] Bapna, R., When Snipers Become Predators: Can Mechanism Design Save Online Auctions? Communications of the ACM, 2003, (to be published) /04 $17.00 (C) 2004 IEEE 8

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