Evidence of in B2B Open, Reverse e- and First Price Sealed Bids *

Dr. Michael R. Mullen, Florida Atlantic University, USA Dr. Tamara Dinev, Florida Atlantic University, USA Dr. John L. Hopkins, Strathclyde University, Scotland Dr. Dennis F. Kehoe, Saxby E-Professor, University of Liverpool, UK

*This research was carried out with financial support from Amerijet International, Inc., Fort Lauderdale, Florida, Florida Atlantic University, USA and the University of Liverpool, UK, The collaboration was organized under the auspices of Sister Cities International, specifically the sister cities of Fort Lauderdale, Florida, USA and Sefton, UK.

ABSTRACT

The rise of the Internet and its role in global business has lead to the development of an emerging electronic marketplace, including business to business markets. This research looks at B2B auctions in e-marketplaces versus more traditional B2B formats. In particular, we compare open, online reverse auctions to first price sealed bids, with a particular interest in pricing strategies. We examine two issues driven by the literature and interviews with purchasing and selling executives. First, we examine whether the online reverse auctioning process is fundamentally different, from a pricing perspective, than first price sealed bids, given sufficient /auction experience. Second, we look at the effects of expected future business on bidding strategies. We use a series of six laboratory experiments with five teams participating in each series of auctions. We manipulate both auction format and the expectations of future business. We find no evidence of a price differential for open, reverse e-auctions versus first price, sealed bids in B2B auction markets. Further, while expectations of future business lowered prices somewhat, the amount was not statistically significant, lending support to the winners curse as a more plausible explanation for firm‟s very low bid prices, often below cost.

INTRODUCTION

Auctions for buying and selling products are long standing mechanisms for establishing market prices in business to consumer (B2C) and business to business (B2B) markets. More recently there has been a substantial increase in online trading, in particular electronic marketplaces. The first e-auctions were apparently conducted via e-mails and newsgroup as early as 1988 (Wurman, 2002). E-Bay, currently the premier B2C and consumer-to-consumer (C2C) Internet auction site, founded in 1995, is generally held as the exemplar for the industry (Keenan, 2000). Several prominent strategist predict that e-commerce will have a huge impact on global markets (Porter, 2001; Drucker, 2001), especially on industrial markets. It is not surprising then that the most promising Internet and e- marketplace applications are in the B2B context. Business to business electronic markets have an impact on the way buyers and suppliers interact and, as a consequence, it is important that we understand the behaviour of the participants on these markets (Grewal et al., 2001). The parties participating in online reverse auctions, buyers and sellers, take different perspectives and are attracted by different benefits. Buyers believe that reverse auctions promise reduced purchase prices, administrative costs, inventory levels (Smeltzer and Carr, 2003), and improved flexibility (Parente et al., 2004), while sellers are enticed by expectation of market penetration and increased business, (Smeltzer and Carr, 2003) and a potential for lower transaction costs (Milgrom, 1989). There are four main types of auctions in the literature (Chakrvarti et al., 2002). The first is the sellers auction, where a manufacturer, distributor, or individual has products or services for sale and offers them to the highest bidder. These are sometimes called open, ascending-bid auctions. This approach is typified by e-bay in the B2C and C2C markets. The second type is the open, descending auction sponsored by buyers rather than sellers (1). These are dubbed

“reverse” auctions because the sellers bid instead of buyers, and prices are bid down instead of up (Jap, 2003). Here the buyer requires products or services, and invites a set of pre-qualified suppliers to bid for the order, under strictly defined auction conditions and regulations, and over a pre-defined time period (Hopkins et al, 2004). The other two auction mechanisms are both closed, sealed bid auctions with the lowest bidder winning. The difference between the two types of sealed bid auctions is that in first price sealed bid auctions, the winner pays their lowest bid. However, in second price sealed bid auctions, the winner pays the price of the second lowest bidder. For the purposes of this paper, only open, reverse e-auctions and first price sealed bid auctions are considered. In the business-to-business marketplace, e-auctions were initially pressed into service as tools to dispose of excess inventory although e-auctions are currently used in the day-to-day operations of many businesses. Companies are using auctions in situations in an effort to extract better prices from their suppliers (Wurman, 2002). They have gained popularity because firms believe they can gain immediate „short term‟ cost savings, simultaneously assisting and simplifying the negotiation process. Indeed, Tully (2000) claims such auctions have achieved gross savings of between 5-40% while Cohn (2000) claims an average savings of 15-20%. While there may have been savings for some firms, the potential savings depend on the previous pricing mechanism, number of bidders, strength or weakness in the supply market, and etc. With multiple potential causes, it is hard to attribute claimed savings to e-auctions without being able to rule out alternative rival explanations such as an increase in the number of bidders, a short supply market or the winners curse. If firms have used traditional sealed bid auctions in the past, there is little theoretical expectation (see below) of price differentials between first price sealed bids and online reverse auctions. However, reduced “transaction costs, …., offer an important alternative explanation to the revenue based approaches for explaining the popularity of specific auction institutions,” (Milgrom, 1989, p.17), especially e-auctions. The way in which the different firms approach online reverse auctions produces different strategies and behaviours (Grewal et al., 2001). The buyer in an open reverse auction must always ensure that there is enough competition present, for the product or service they require, within the particular marketplace at that particular time, in order to obtain a market driven price. On the other hand, the bidder tries to predict the behaviour of the other bidders. Each bidder makes an estimate of their own value and also an estimate of what others will bid. Good bidding is often the result of correct predictions about the behaviour of others and sometimes that means guessing the extent of someone else's information correctly (Mester, 1988). In many aspects, however, the progress of auction research is still incremental and slow (e.g. Van Tulder and Mol, 2002). The literature on auctions in general, and e-auctions in particular, is not yet well developed with a limited number of theoretical and empirical papers (Grewal et al., 2001) resulting in calls for more research on auctions (Bazerman, 2001; McAfee and McMillan, 1987) and the behaviour of participants in different types of auctions and under different scenarios (Chakrvarti et al., 2002). Specifically, they call for experimental research that manipulates bidding for real objects that account for “sequential dependencies … that drive rates and patterns of adaptive learning,” (p.291). The research described in this paper examines two central issues that emerge from the literature, our interviews in the field and from a series of pre-test experiments. We examine whether the open online reverse auction process is, in fact, fundamentally different from sealed bids, given that bidders have sufficient experience in their industry and with bidding/e-auctions. Second, we examine what influence, if any, the added incentive of potential future/repeat business has on auction bidding strategy, relative to the winners curse. The paper is organized as follows. Next, we discuss theory development, followed by methodology, including experimental design, pre-tests, manipulations and then the results from the six sequential experiments. The results are followed by the discussion, including contributions to theory and practice and suggestions for future research and the conclusion.

THEORY DEVELOPMENT

William Vickery's (1961) seminal paper demonstrated that all four auction formats described above produce the same revenue. Vickery‟s revenue equivalence theorem generally holds that the expected revenue from any auction mechanism equals the winning bidders' expected marginal revenue. In essence, the four basic auction types outlined

above are theoretically expected to yield the same expected revenue (Lucking-Reiley, 1999). Pertinent to this study, William Vickery showed that, “under strong simplifying assumptions, competitive bidding in an open auction would produce the same expected revenue as in a sealed bid auction” (Riley, 1989, p.41-42). In B2B markets, the relatively new open online reverse e-auctions are complementary to, or substitutes for, more traditional sealed bid processes. Buying firms pre-qualify potential bidders in both scenarios and establish a deadline to submit a bid. The differences are that “In contrast to open auctions, participants in sealed bid auctions submit their bids without seeing others‟ bids,” (Chakravarti et al. 2002, p.283) and, typically, they are present at the tendering and opening of the bids. In both formats, the bidder with the lowest price wins. “Hence, the bidding strategies and the mapping from strategies to outcomes for the two auction mechanisms are identical and they share the same (Nash) equilibrium (Chakravarti et al. 2002, p.284) theoretically creating strategic equivalence. Nonetheless, reductions in “transaction costs …. offer an important alternative explanation to the revenue based approaches for explaining the popularity of specific auction institutions” (Milgrom, 1989, p.17). Contrary to the revenue equivalence theorem, empirical studies have either found price differentials between auction formats or they were inconclusive. Early laboratory results indicate that bidders typically bid higher in sealed bid auctions compared to open descending auctions (Cox et al., 1982; Cox et al., 1983). Smith‟s (1982) experiments found no conclusive evidence for or against revenue equivalence. On the other hand, Lucking-Reiley found that open descending auctions (Dutch) “produce 30% higher revenues than the first price auction format, a violation of the theoretical prediction and a reversal of previous laboratory results” (1999, p.1063). Massad and Tucker (2000) also found that in online auctions with reserve prices, mean sales prices were higher than in in-person auctions. Chakravarti and colleagues call for “more research to explain these observed failures of strategic equivalence” (2002, p.288). Consistent with the revenue equivalence theorem, we hypothesise that in B2B markets: Hypothesis 1: In B2B auctions, there will be no price differential between open, reverse e-auctions and first price sealed bids, amongst experienced bidders.

The evidence also suggests that bidders in first price sealed bid and open, descending auctions often bid less than their marginal value (McAfee and McMillan, 1987). This is sometimes referred to as the “winners curse.” Milgrom see the winners curse as “one of the surprising and puzzling conclusions that have been turned up by modern research into auctions” (1989, p. 3) Unfortunately, the winners curse is seen as pervasive for inexperienced bidders in sealed bid auctions (Kagel et al., 1987) although experience apparently reduces the magnitude of the „curse.‟ Tenorio (1993) argues that in repeated auctions, bidding behaviour is likely to be influenced by learning. Further, Hopkins et al. (2004) note that participants in auctions exhibit a certain level of learning, in terms of the strategies they employ and their bidding patterns, as their auction experience grows. In our online reverse auction pre-tests, described more fully below, substantial numbers of bidders bid below known cost to win the right to sell 100,000 mobile phones with the rational expectation of future, repeat business. If there is reason to believe that winning the bid now will lead to future sales, it is rational to bid below cost to secure the business. Brands are believed to build loyal customers that yield high market share and a continuing stream of income for the brand owner (Aaker, 2000; Keller, 2002). The importance of brands and their value to firms in terms of inducing brand loyalty and reducing customer „switching‟ has long been an important area of enquiry by marketing academics (i.e., Tucker, 1964; McConnel, 1968; DuWors and Haines, 1990; Venkatech and Mahajan, 1997). “The idea of brands as a core asset upon which corporate success depends is deeply ingrained in modern corporate culture as well as being a central tenant of the marketing discipline” Muzellec et al. (2004). Further, for mobile phones it is reasonable for experienced mobile phone users to expect that consumers are subject to switching costs of time and money (cords, adapters, etc.). An alternative explanation for bidding very low, even below cost, is the “winners curse.” While we recognize the „winners curse‟ as a plausible explanation for low priced bids, we expect the prospect of future sales to be a greater driver of low price bidding than the winners curse. Hypothesis 2: When supplier firms have an expectation of future business based on winning the current bid, their bid prices will be lower than they would be to sell an equivalent product with no expectation of future business.

METHODOLOGY

The experiments model a realistic environment, with bidders using a licensed version of an Oracle e-auctioning software tool, with access via a browser based client interface and configured specifically for the purpose of the extraction of accurate primary data. They are designed to replicate a commercial sourcing/selling situation where five companies are invited to bid to sell 100,000 mobile phones to the fictitious Zenon Corporation based in the United Kingdom. Following typical research practice, we analyze “symmetric” environments where competitors can not discern differences among themselves (Milgrom, 1989). Prior to the auctions, Zenon Corporation is assumed to have carried out all necessary pre-qualification activities. This ensures that all companies can supply the product at an acceptable quality and that all technical queries relating to the product have been resolved and shared amongst the bidders. The only remaining decision criterion for selecting the winning firm is the lowest bid price. This ensures that the buyer is prepared and willing to place a contract with any winning bidder. The participants consisted of a mixture of undergraduate and post graduate business students from a major South Eastern university in the USA. For each experimental session, both in the pre-test and in the following six experiments, ten participants were divided into five teams of two, with each pair representing a different vendor company that has been pre-qualified and invited to bid for the business. Milgrom makes the case that in small stakes laboratory experiments, it is advantageous to have “pairs of subjects who must place a joint bid; (as) this is likely to encourage discussion of how to bid” (1989, p. 7). One person in each team was assigned the role of Finance Director, and the other the role as Sales Director. Some participants had previous C2C or B2C auction experience, primarily from e-Bay, however, none of the participants had previous experience with B2B auctions of any kind.

Experimental Design Pre-tests Contribute to Theory Development and Study Design An ad hoc series of exploratory laboratory pre-tests were conducted among five selling “firms” vying for the buyers' business. The first pre-tests began with a traditional sealed bid followed by two successive e-auctions. Initially, in the second round of e-auctions, the final bid times were extended three minutes without warning. The feedback on the unannounced time extension was immediate and clear. Changing the rules was considered dishonest/unethical/cheating, trust was compromised and so was interest in doing business with Zenon Corporation. A new series of pre-tests eliminated the time extensions with a traditional sealed bid followed by two or more successive e-auctions. The results of those pre-tests uniformly showed a significant reduction in the winning e-auction bid relative to the initial winning first price sealed bid. While this tentative observation was in contrast to our hypothesis that open descending auctions and first bid sealed auctions produce equivalent revenue, it was consistent with other reported results (e.g., Lucking-Reiley, 1999; Massad and Tucker 2000) and those generated under similar laboratory conditions in the UK (Hopkins et al., 2004). Nonetheless, these ad-hoc pre-test observations are contrary to the existing theory and to other reported observations (Mullen, 2004; GMPH, 2006). In the series of pre-test experiments, many of the firms bid below known cost to win the business. Based on retrospective interviews, participants indicated that the scripts and their experience with mobile phones as customers lead firms to bid below cost because of the expectation of future business. Important drivers of the firms willingness to bid below costs were the effects of brand loyalty and switching costs that „implied‟ almost certain repeat business(2). In addition, we observed that many of the bidding teams, bid less often and later as they gained experience through the three sequential rounds of e-auctions. We designed and conducted six experiments within a computer laboratory setting where conditions (auction format and expectations of future sales) are manipulated to further study these issues.

Experimental Manipulations The first hypothesis requires us to disentangle the effects on revenue from the two auction formats, open reverse e-auctions and first price sealed bids. In previously published research (Hopkins et al., 2004) and in our experimental pre-tests, first price sealed bids were followed by two or three open, descending e-auctions for the same products in the same environment. The results across those experiments consistently produced lower bid prices from suppliers in the final round of e-auctions, compared to the initial sealed bid. An alternative explanation for the lower priced bid results

is that learning occurs across successive rounds of three and four auctions. As noted in our pre-tests, we observed that bidders gain experience, changing their behaviour in later rounds of auctions on timing and frequency of placing bids, lending some credence to the notion that experience effects auction strategy. In order to disentangle the effects of learning on pricing from the effects of auction format, we organized six experiments, each consisting of five supplier firms bidding five times to sell the same products. All six experiments begin with a first price sealed bid auction. The initial sealed bid is followed by three successive open, reverse e-auctions under identical circumstances. The three rounds of e-auctions were open for bids for 15 minutes each while the sealed bid auctions were closed in nature with each team allowed to submit only one „closed/sealed‟ bid before the prescribed deadline. Finally, the same five supplier firm‟s are asked to bid to sell the same volume of the same product with the same competitors and circumstances in a first price sealed bid auction, but to a different buyer. This approach provides for a straight forward assessment of the revenue equivalence theorem and our first hypothesis. If the final e-auction (4th round of bidding) and final sealed bid (5th round) prices are different, then revenue equivalence does not hold. However, if there is no price difference between the two auction formats, that would provide support for revenue equivalence. We also seek to disentangle the effects of expected future business from the winners curse. To test hypotheses two on the influence of expected future business on bid strategy relative to the winners curse, we manipulate the scenarios, giving bidding firm‟s two very distinct scripts. The first script stresses the potential for future or repeat business for the winning bidder. The second makes it clear that no future business is implied based on the bid at hand. With this manipulation, we are able to observe if the experience with the auction process and the „industry competition‟ had any influence, what so ever, on the price relative to the first price sealed bid auctions versus the open reverse e-auctions. If there is no price differential between the two auction formats after four and five rounds of bidding, that result would provide some evidence that low prices may be driven by the winners curse. Alternatively, if bid prices are lower in the scenario promising future business, that result would lend some support to an alternative rival hypothesis for low bidding strategies, at least under similar circumstances. The scripts read as follows: Expectation of future business: “Predictions show that the long term market will 'shake out' and profitability will increase for firms with a higher market share. Customer brand loyalty will create positive word of mouth and repeat purchase behaviour. In addition, significant switching costs (i.e., new accessories, re-entering data, learning a new system, etc.) will discourage the customer from switching away from the supplier winning the auction in the future.” No expectation of future business: “The sales from this auction are 'private label.' The purchaser requires the supplier to install the purchaser's own label/brand on the phones. The manufactures label is private with no prospect of brand loyalty from the phone customer, or any future business, accrues to the manufacturer.” The firms in the first two experiments all received scripts emphasizing future business while in the next two experiments, all five firms received the second script with no expectation of future business. The next two experiments started with the “future business” script and switched to the no expectations script after the third auction. Table 1 presents the manipulation schedule for the series of six experiments. In all six experiments, the pattern of auction format remained identical: We begin with a first price sealed bid, followed by three open reverse e-auctions and last, a final first price sealed bid.

Table 1: Experimental Manipulations: Open, Reverse e-Auctions versus First Price Sealed Bids and Expectations of Future Business, or Not. Experiment Sealed Bid e-Auction e-Auction e-Auction Sealed Bid Auction # 1 # 2 # 3 Auction # 1 future business future business future business future business future business # 2 future business future business future business future business future business # 3 no expectation no expectation no expectation no expectation no expectation # 4 no expectation no expectation no expectation no expectation no expectation # 5 future business future business future business no expectation no expectation # 6 future business future business future business no expectation no expectation

The Laboratory Process Changing the parameters within the scripts provides the ability to assess various types of auction situations. So although this particular experiment is concerned with a product based in the telecoms market, the design of the experiment is such that it could just as easily model any type of auction situation, regardless of industry, geography or ownership. As such, two general scripts were distributed to each two person team that identify their industry and firm (i.e., Sanchez Semiconductors), provide auction information/rules and a instructions for their individual roles, either as Finance or Sales Director. Those scripts are the same for all firms in the six experiments creating a symmetrical environment for competition during the auctions although but participants were unaware of this fact as they only had access to their own company information.. The script for the Finance director Manufacturing included information on cost per phone of ₤20 (+ 10%), the expected overhead contribution of ₤2.00 and the normal selling price of ₤60.00. The scripts for the sales director were essentially the same except that they state the firm‟s annual target profit is ₤ 2 million and that the sales director will receive a bonus (commission) of ₤15,000 from winning this auction. Both scripts contained the same bidding rules: “As part of the pre-qualification all bidders have agreed to abide by the following rules: 1. Bids must be placed for the cost of supplying one unit (one mobile phone); 2. Bids must be placed to supply all 100,000 3G mobile phones; 3. Bidding lasts for 15 minutes only; 4. Bids must be placed in decrements of at least ₤0.20; 5. Multiple bids are allowed and 6. The lowest bid wins. During the open reverse auction, each team/firm could see, via an on-screen auction control panel on their computer monitor, the status of their own bid, and the lowest bid, at all times, but could not see the identity of the current leading bidder, unless it was themselves. The identification of all the bidders was available only to the buyer. In addition, details of the auction, including an accurate countdown timer and closing time, were displayed on the screen. During each experiment, the researchers were available to assist with placing bids on the system, navigating between screens, and viewing the data presented to the bidders. The system automatically kept records of all bids.

RESULTS

The results of the six experiments are shown in Tables 2 through 7 below. The final bids, in ₤s, are listed for each firm in each auction format with the lowest bid in bold. You can also see the bid prices for firms expecting to gain future business from winning and from those with no expectations. Hopkins et al. (2004) found that online reverse auctions consistently result in lower final bid prices by firms in comparison with their initial sealed bids. In our experiments, we also saw lower selling prices in open, reverse e- auctions compared to the initial first price sealed bids. However, when the data are compared for experienced bidders, after rounds four and five, the differences disappear. There is no statistically significant difference (n =60, p<.000) between the final open, reverse e-auction bid prices and the final, first price sealed bid prices, across all firms and settings. There is also no statistical difference in the six winning bids in the final e-auctions and six winning bids in the final first price sealed bids (n =12, p<.000). These findings lend support to the revenue equivalence theorem and to Hypothesis I.

Table 2: Experiment 1 Bidding Sealed Bid e-Auction 1 e-Auction 2 e-Auction 3 Sealed Bid Firms future business future business future business future business future business Galligan 50 28.8 75 29.77 28 Ides 31 28.5 27 27 24.5 Sanchez 30 27.5 27.4 35 29.9 Domain 33 24 34.8 26.6 26.6 Chatham 28.8 25 28 22 27

Table 3: Experiment 2 Bidding Sealed Bid e-Auction 1 e-Auction 2 e-Auction 3 Sealed Bid Firms future business future business future business future business future business Galligan 35.2 32.15 27.8 23.6 32.15 Ides 31 27.7 31.2 25.9 33 Sanchez 30 25 60 23.8 30 Domain 29 29 23 26 24 Chatham 54 27 25.8 25.4 25

Table 4: Experiment 3 Bidding Sealed Bid e-Auction 1 e-Auction 2 e-Auction 3 Sealed Bid Firms no expectations no expectations no expectations no expectations no expectations Galligan 35 27.5 32 34 33 Ides 25.2 25.5 24 46 32 Sanchez 36 28 23.99 35.99 34 Domain 45 29.2 23.5 30 35 Chatham 35 27.6 22.99 34.99 35

Table 5: Experiment 4 Bidding Sealed Bid e-Auction 1 e-Auction 2 e-Auction 3 Sealed Bid Firms no expectations no expectations no expectations no expectations no expectations Galligan 45 22.1 22.2 22.1 22.1 Ides 32.2 29.8 32.8 22.5 40 Sanchez 32 .. 22.1 18 18 Domain 38.99 29 40 38 38 Chatham 52 22 38 28.9 28.9

Table 6: Experiment 5 Bidding Sealed Bid e-Auction 1 e-Auction 2 e-Auction 3 Sealed Bid Firms future business future business future business no expectations no expectations Galligan 54 19 21 19.74 20.2 Ides 32 60 33 19.76 27 Sanchez 45 16 10 19.98 19.98 Domain 44 17 22 19.96 25 Chatham 42 21 20 32 32

Table 7: Experiment 6 Bidding Sealed Bid e-Auction 1 e-Auction 2 e-Auction 3 Sealed Bid Firms future business future business future business no expectations no expectations Galligan 33 23.1 22.9 34.8 34.8 Ides 32 21 20.8 25 23 Sanchez 34 32 21.5 24.5 24.5 Domain 40 23 18.5 21 25 Chatham 34 31 31 31 30

In evaluating Hypothesis II, we find some support in the direction and percents of the price changes but the differences are not statistically significant. The average final e-auction bids in experiments (one and two) where there were clear expectations of future, repeat business, is 27.4₤ per phone compared to28.6₤ in experiments (three and four) where it was clear there was no expectation of future business. Given no prospect of future business, bid prices rose an

average of 4.4% but the increase was not statistically significant (n=20, p< .000). In experiments (five and six) the expectation of future business was initially present, but was taken away after the 2nd e-auctions. With future business expectations, the average bid price is 22.4₤ but when the prospect of future business was removed, the average bid price only rises 5.4% to 23.6₤, which is not statistically significantly different (n=20, p<.000). Further, the average final reverse e-auction bid for all firms in the six experiments with future business expectations were 14.8% lower than the average final bids all firms with no expectation of future business, however, the differences are not statistically significant (n=40, p<.000). These results indicate that, on average, the prospect of future business lead to modest price reductions of 4 to 6%, but hose reductions are not statistically significant. Therefore, our data and analysis offer little support for Hypothesis II. It appears that the winners curse may have more impact on low bidding behaviour than the prospect of future business, regardless of how the data from the manipulations are analyzed.

DISCUSSION

In terms of the strategies adopted by the teams, and the tactics employed, it was observed that the most commonly used strategy, in the early rounds, was that of simply “reacting to how the other teams bid.” As the rounds progressed, and the teams gained a greater level of experience, the more “last minute bidding.” Similarly, Roth and Ockenfels (2000, 2002) developed a game-theoretic framework where they considered the benefit and the risk of bidding at the last moment -- the benefit being that the bidder does not reveal his/her private valuation to others and the cost being that the bidder risks his/her bid not being registered on time with the auction site. This is optimal for the bidder, as they do not reveal any information to the other bidders during the auction process (Subramaniam et al., 2004).

Theoretical Implications An important contribution of the experiments is to show that there is no difference in revenue outcomes between first price sealed bid auctions versus open, reverse e-auctions amongst experienced bidders. As the firms' „managers‟ gained experience of the auction process and knowledge of the „market‟, the price differences between forms (i.e., final e-auction bid and final sealed bid) were trivial and not significant. This provides some support for the revenue equivalence theorem and is consistent with Jap‟s (2003) observation that cost savings are not systematically related to open versus a sealed bid format. Nonetheless, more research is needed to further assess this finding in order to determine under what conditions, a sealed bid may be more cost effective than the use of an e-auction platform. During the pre-tests, we observed teams bidding below cost. This phenomenon had two potential explanations. The first is that open reverse e-auctions were forcing bidders to drive down offer prices. The alternative explanation is that the pre-test scripts lead firms to believe that brand loyalty and switching costs would lead to future business. In that case, short term profit sacrifices may lead to long term market share gains. Our results provide little support for the later explanation. Firms that had a rational expectation that winning „the‟ bid would lead to future business, bid lower prices to capture that future business than firms without those expectations, but the price difference was small and not significant.

Practical Implications: When should a Buying Firm use open online, reverse auctions? Jap (2002) suggests that there are three conditions that are critical to the decision for engaging in reverse online auctions. These three conditions are the product characteristics, sourcing strategies and supply base characteristics. Online reverse auctions are appropriate for procurement of price-based products, when spare capacity exists in the supply base and competition among suppliers is intense. Smeltzer and Carr‟s (2003) interviews with practitioners also indicate that appropriate supply market conditions must exits for a successful online reverse auction. That is, if the market is in short supply, then an e-auction may not be efficient, especially with established suppliers. On the other hand, if the market has an excess supply, then vendors may have little choice but to participate in e-auctions, potentially driving prices lower. Smith (1989) argued that future auction research needs grounding in real world auction experiences. To try to better understand the results from these experiments relative to the “real world,” we interviewed the global sourcing

director of a major mobile phone manufacturer based in the United States. In her role, she operates an e-marketplace and has very substantial experience with online reverse auctions. The firm sits firmly on the buy-side of the e- marketplace and does not use it as a mechanism for selling. We will refer to this global, mobile phone manufacturer as GMPH (a fictitious acronym) and to GMPH (2006) to refer to the interview with their global sourcing director. GMPH outsources 100% of the components to make its mobile phones. The e-marketplace, and in particular the ability to operate reverse auctions online, has assisted in their quest to lower prices. The e-market place facilitates greater geographical reach than was previously possible before online functionality was available. However, it is worth noting that online reverse auctions still only represent 5-10% of their overall procurement. GMPH‟s sourcing director indicated that e-auctions are not necessarily suited to all types of sourcing. She considers their component base as being divided into three categories; off-the-shelf, semi-custom and custom, with only the off-the-shelf items being sourced via e-auctions in any great quantity. She believes that face-to-face negotiations and the increased strength of relationship they bring are more important than simply price when procuring more complex components. Mullen (2004) noted that it is not uncommon for telecommunications contractors to wait until the last minute to complete and seal their bids or to choose between alternative proposals for submission in first price sealed bid auctions. GMPH (2006) also observed that experienced open, online reverse auction bidders, only bid once at the last minute mimicking the sealed bid process. From the supplier perspective, a principal benefit from e-auctions versus sealed bids is eliminating travel expenses for those bidders who want to wait to the last seconds to bid. Lucking-Reiley (1999) noted some time ago that use of the Internet for auctions lowers transaction costs. GMBH‟s view is that the online process is most efficient with globally dispersed suppliers, as it obviates the need for expensive, time consuming travel on the part of the suppliers seeking to attend a traditional „sealed bid opening.‟ Further, GMPH (2006) found price differences between the two formats to be minimal, if any, lending support from the field to our laboratory results and the revenue equivalence theorem.. In summary, reducing transaction costs for the suppliers, not cutting costs for the buyers, is an important outcome of the e-auction process relative to the sealed bid process. Based on the literature, interviews (GMPH, 2006; Mullen, 2004) and our results, we identify four parameters that determine when an online reverse auction will most likely be of benefit to buying firms in B2B markets: 1) are the suppliers domestic or globally dispersed; 2) what type of product or service is being sourced (commodity, semi-custom or custom)?; 3) are there a large or small number of competitors and 4) what is the current supply condition in the marketplace. If the suppliers are globally dispersed, the product or service is a commodity, there are a large number of suppliers and the supply in the market is long, then it may well in the buying firm‟s best interest to use an e-auction. If the supplying firms are globally dispersed, it may be in their interest to submit bids through and e-auction platform, rather than through a traditional sealed bid process, regardless of the market conditions to save on transaction costs.

Future Research Future research might explore the issue of the effects of market supply conditions on the results of online auctions versus sealed bids. In line with the decision model above, it is rational to expect that in tight supply conditions, it may be detrimental to the buying firm‟s interests to try to use e-auctions to drive down price. Vendors with a choice may skip the process and move to negotiate prices in a private, relationship based environment. On the other hand, excess supply in the market of commodity products may lead to substantial price cutting in an auction environment, relative to negotiations. In that case, e-auctions may be a better vehicle than sealed bids to drive prices lower because they reduce transaction costs for global suppliers, reducing disincentives for participation.

CONCLUSION

In our research, we find no substantial or statistically significant price difference between the last round of the open reverse e-auctions and the final first price sealed bids. This finding calls into question the popular notion that e- auctions automatically lead to lower prices. Our data and results are consistent with, and lend support to, the revenue equivalence theorem. The expectation of future, repeat business, under these experimental conditions, was thought to be acting as an incentive for suppliers to lower their bid prices, sometimes even below cost, in online reverse auctions.

Participants seemed to believe that losses in the short term might lead to greater gains in the long term. While the bidders lowered their bids somewhat, the differences were not statistically significant, thus lending more support to the winners curse as an explanation for unusually low bids. Nonetheless, the discussion on when it is beneficial for buyers to use online, reverse auction for procurement provides some guidance to managers as to when the buying firm is most likely to benefit more from the -marketplace relative to traditional sealed bids or negotiations. An open question is whether those firms who have reported achieving significantly lower prices in online auctions in the past, have faced excess supply in the market, inexperienced bidders, or some other phenomenon not captured in our results. Obviously, much more work remains to be done.

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Notes (1) In “Dutch” descending auctions, the seller posts a constantly declining price and the first buyer to accept the given price wins the auction. In the reverse auctions considered in this study, bidders may keep bidding until the auction reaches a scheduled conclusion. (2) In the pre-tests, the sales directors of the selling firms were instructed that “The Long-term market will shakeout after sales and customer brand loyalty will lock-in.”