Reverse auctions for relationship marketers

Industrial Marketing Management 34 (2005) 157 – 166

Reverse auctions for relationship marketers Shawn P. Dalya,*, Prithwiraj Nathb,1 a b

Graduate and Online Education, Tiffin University, 155 Miami St., Tiffin, OH 44883, USA Xavier Labour Relations Institute, Circuit House Area (East), Jamshedpur-831 001, India Accepted 31 July 2004 Available online 18 November 2004

Abstract Reverse auctions in logistics and procurement have grown dramatically since the advent of widespread Internet usage in the late 1990s. A literature review indicates that scholars and practitioners are reaching a consensus around a trade-off between the value and benefits of gaining lower prices versus losing long-term relationships with suppliers. Yet at the same time, a quiet evolution has come about in the economics and management literature, opening the way for new, more relationship-friendly auction designs. Based on this new work, a series of guidelines and principles are developed which describe how managers may collect the economic pricing advantage of reverse auctions— yet retain the long-term benefits of relationship marketing. D 2004 Published by Elsevier Inc. Keywords: Reverse auctions; Relationship marketing; Procurement

1. Reverse auctions and relationships: mutually exclusive? The present issue of Industrial Marketing Management consists of a number of different aspects of pricing in business-to-business relationships. Our focus is on procurement via the Internet, which is huge, most likely representing more than US$1 trillion in 2003 (Mendoza, 2002). Of the major e-procurement methods, the reverse auction has attracted the most interest because of its ability to be quickly implemented and provide process improvement benefits for participating suppliers and buyers (Smart & Harrison, 2002). Unfortunately, reverse auctions are also accused of destroying buyer–supplier relationships, making relationspecific investments increasingly unlikely, raising the longterm costs of supply (Smeltzer & Carr, 2003).

* Corresponding author. Tel.: +1 419 448 3404. E-mail addresses: [email protected] (S.P. Daly)8 [email protected] (P. Nath). 1 Tel.: +1 91 657 225 506x512. 0019-8501/$ - see front matter D 2004 Published by Elsevier Inc. doi:10.1016/j.indmarman.2004.07.013

Often, when outsourcing a product or service, the choice is put forth as a dichotomous option: either long-term supplier relationships or competitive spot bidding. It is becoming clear that many believe the existence of trade-offs between the short-term tactical efficiency benefits and potential longterm relational and strategic losses (Van Tulder & Mol, 2002). Given this forced choice, a wide-ranging crossindustry survey of plant managers indicates that relationships and negotiation is used much more frequently than bidding (Heriot & Kulkarni, 2001). This would indicate that the benefits of long-term, cooperative, relational planning and strategizing must outweigh the pricing benefits of an auction. But is the issue so bblack-and-whiteQ? That is, can auctions be made more relational and long-term oriented? The purpose of the present paper is to explore ways in which reverse auctions can be made friendly to relationships, yet retain their valuable pricing benefits. First, a brief review of auction design basics is undertaken. Next, reverse auction limitations are identified from the existing marketing literature. Then, advances in auction theory are examined for ways to improve designs. Finally, discussion is undertaken of where these advances take the future of auctions and supply chain relationships.

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2. Auction basics In a reverse auction, a buyer creates a request for quotation (RFQ) that describes the details of the requirements and posts the RFQ to the marketplace. Vendors prepare and submit bid packages that include the asking price and other details like their proposed method of handling the project. The buyer typically has several criteria for choosing a bidder like price, quality, delivery date, and payment terms. Depending on the fit among such criteria, the buyer chooses the best bid. The advent of the Internet has totally transformed the auction process. Online auctions differ from their physical counterpart in several ways, like reduced transaction costs for both buyers and sellers; accessibility to more participants by reducing restrictions like geographical proximities; ease of setting the durations of the auction process depending on the product and/or service being transacted; no fixed time of entry for the participants; ability to dynamically alter the auction lot size. Internet auctions even have a phenomenon called buy-out pricing that is a declared price at which the auctioneer is ready to forego the auction and sell off the item immediately (Lucking-Reiley, 2000). Thus, reduced cost of running the auction online has made it an alternative purchasing and/or selling channel previously unavailable to buyers and sellers. Issues like the impact of auction design on the reservation price, minimum bid requirement, lot sizes, time duration and particularly, the long-term costs and the benefits of online auction as a mode of products and services procurement channel in contrast to traditional buying/selling process in business to business (B2B) context has become significant in the academic and the practitioner’s world. Each of these areas will be examined in this section (summarized in Table 1). 2.1. Auction form and price acceptance There are many different types of auctions to choose from; some of the popular forms of auctions are the open ascending price or English auction; its counterpart the open descending auction or Dutch auction; and the closed bid first-price and second-price auctions. English auction starts with a low price called the minimum bid and any bidder staking a counter claim should exceed the previous bid by the minimum bid increment. The seller (buyer) can specify some reserve (ceiling) price that is the minimum (maximum) sale price that is acceptable.2 Dutch auction, a regular feature in the market of perishables such as the 2 In common with other auction researchers (Wang, 2000), we make the assumption that buying/selling situations are equivalent. That is, the circumstance of a seller in a forward auction is equivalent to the procuring firm in a reverse auction, and vice versa. Hence, some of the empirical results we cite are from forward auction research restated in reverse auction format.

Dutch flower market, works via a descending price mechanism. The initial price called for is quite high and it is gradually lowered until a winning bidder is found. In sealed bid first-price auction, the bidders submit bids in sealed envelopes where the highest bidder wins and the winner pays the amount bid. The sealed bid second-price, also known as Vickrey auction, the highest bidder wins but pays the second highest bid (Vickrey, 1961). While comparing the revenue from Vickrey auctions with other major forms of auctions, like English, Dutch and firstprice sealed bid auctions, Vickrey noted that all of them yielded the same expected revenue to the auctioneer. This is called the Revenue Equivalence Theorem in auction literature.3 Lucking-Reiley, however, found that Dutch auctions are 30% more profitable for the auctioning firm in the case of Internet auctions of collectible trading cards (Lucking-Reiley, 1999). In turn, Tenorio finds that first price auctions are significantly superior to lower price auctions (1993), like the second price design. 2.2. Pricing design Basic auction theory, backed by empirical research, says that the more bidders which come to an auction, the lower the expected procurement cost (Tenorio, 1993). Intuitively, this makes sense; more competition drives down prices. Thus the central focus of basic auction design is setting up the auction in such a way that attracts the greatest number of bidders. In the following sub-sections, we discuss design elements critical to attracting more bidders and achieving lower final price. In procurement auctions, setting a low initial bid level decreases the chance of making a sale (Lucking-Reiley, 1999). This is because a high starting bid encourages bidder entry into the auction. The reason is that bidders do not enter an auction until expected profits are equal to the costs of participating (Harstad, 1990; Levin & Smith, 1994). Thus, high initial starting points in procurement auctions increase the likelihood that selling firms will have a profit margin. With a low starting price, auctions will close at the starting bid itself or do not register any bid at all. In such cases, the buyer’s cost of setting up the auction is incurred without any revenue. A ceiling price is the higher bound placed by the seller on the closing price. Sometimes it is made public by the seller (the minimum bid) and sometimes it is kept secret, particularly for high value objects (Bajari & Hortacsu, 2000). It is a protective mechanism for the buyer so that the reverse auction does not end at a very high price. The seller can also specify a minimum bid increment each time a bidder places his bids. Lower minimum bid requirements can reduce overall bidding activity by reducing number of participants and thus increase procurement costs (Vakrat & Seidmann, 2001). 3

A simple proof of the theorem is available (Klemperer, 1999).

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Table 1 Procurement auction design basics Design element

Alternatives

Characteristics

Theoretical results

Empirical observationsa

Form

English Dutch First-price Second-price Starting price Reserve price

Ascending price Descending price Price at highest bid Price at 2nd highest bid High or low High or low

Equivalent outcomes

Lot design

Size Combination

Large or small Homo/heterogeneous

Large produces lower prices

Duration

Open period Close of bidding

Long or short Hard (fixed) or soft (flexible)

Optimality depends on rate at which bidders arrive in the auction

Dutch auctions are superior for the auctioning firm First price auctions are better for the auctioning firm Starting prices and bid increments must be set with sufficient room for auction to flow effectively Price appears lot size independent; may be determined by the difference between maximum and minimum of supplier’ item costs Longer produces lower costs; yet rapid fire bidding (sniping) occurs frequently at the end of online auctions

Price acceptance Pricing design

a

Equivalent outcomes Too high risks no bids

Empirical results are not valid for all circumstances, but are indicative of potential differences.

A second price starting point design issue is the bid increment, the smallest amount that a bid can supercede an earlier bid. Bapna, Goes, and Gupta (2000) discovered in studying multiunit Internet auctions that minimum bid increment does have an impact on the auction. Their work indicates that large bid increments are worse for procurement auctions because selling firms are less likely to make bids near their estimations of costs. That is, large bid increments leave room between the existing bid price and their valuation of the costs of production. 2.3. Lot design Auctions can be classified on the basis of the number and variety of items being auctioned. Single-unit auction considers one unit of a particular item, whereas multi-unit auction refers to multiple units of the same item. When multiple items and their combinations are simultaneously put up on sale, we call it combinatorial auction. In most industrial procurement auctions, a buyer wishes to outsource multiple heterogeneous items from a pool of suppliers with no single supplier having enough capacity to fulfill the buyer’s entire order. In such situations, theoretical results pertaining to isolated single item auction do not hold true. In a multiunit auction, the critical decision a buyer makes is how to make smaller bundles (lots) of their material requirement such that a capacity constrained supplier can provide. Pinker, Seidmann and Vakrat (2003) propose price is a decreasing function of lot size and is determined by the range or the difference between the maximum and minimum of producers’ costs of the auctioned items. Bapna et al. (2000) analyze multi-unit Internet auctions and compare it with multiunit Vickrey auctions, developing a regression model for the revenue generated by multiunit Internet auctions. Unfortunately, they note that the effect of lot size is insignificant. We speculate their result is from examining consumer buyers rather than industrial producers. Thus the theoretical result,

and intuitive logic, of large auction lot sizes leading to production economies of scale may still hold. 2.4. Auction duration Traditional auctions start with a fixed number of bidders and no bidder is allowed to join the auction while it is on. For Internet based auctions, bidders can join any time. Duration of the auction is important, as the number of bidders and ultimately the financial outcome depends on that. Profit of an auctioneer is a unimodal function of auction’s length and the number of units on auction, and the optimal duration and lot size depend upon the rate at which bidders arrive in the auction. Lucking-Reiley’s empirical research confirms that longer auctions do indeed drive down final price (1999). To decide on the duration of the auction, two models are followed. One in which there is a specific rigid deadline and no bids are accepted past that time (also called hard close). The second approach followed is the going, going, gone variety where auction continues past the deadline as long as there is enough bidding activity (soft close). Last minute bidding activity or bsnipingQ is a common phenomenon for Internet based auctions (Lucking-Reiley, 1999). This implies that hard closes are preferred, since they force the last minute activity to occur.

3. Supply chains, relationships, and auctions Although the success of online reverse auctions has helped firms to diversify their supplier base, hedge risks and obtain lower cost procurements, it has also raised several important issues related to its role in maintaining relationship with the partners in the supply chain. One of the major stumbling blocks to leverage such emerging technology is how it can affect long-term business relationships with the supply chain partners developed over the course of many years. Thus, it is vital to ensure reverse auction design is

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compatible with relationships developed with the supplier base. Two types of relationship develop between a buyer and a seller in the context of online reverse auction. One is transactional exchange that is adversarial or at least armslength in nature. Here, both the buyer and the suppliers compete for a fixed pie of benefits. Contracts are short term, no guarantee of repeat business after the contract period, and the supplier is selected solely on the basis of comparative price. The other type is the relational exchange, where the buyer and the suppliers actively create pie-expanding opportunities together (Jap, 2003). In this case, the suppliers are selected not solely on the basis of price but a multiattribute selection criterion like quality, reliability, and congruency with business goals is used. Suppliers make long-term investment to the requirements of the buyers with substantial expectations beyond the initial contract period. Critics argue that online reverse auctions hinder collaboration in relational context (Emiliani & Stec, 2001; Smeltzer & Carr, 2003). Yet, some empirical research indicates suppliers are willing to make specific investment for the buyer’s business in sealed bid auctions. Both new and existing suppliers show commitment towards the buyer (Jap, 2003). Carter, Kaufman, Beal, Carter, Hendrick et al. (2004) show that two-to-one buyers feel that online reverse auctions improve supplier relationship in terms of increased trust, greater access to supplier data and more business for the suppliers. But on the whole, online reverse auctions are not well received by suppliers (Emiliani & Stec, 2001), requiring the buyers to bsellQ the auction to suppliers (Smeltzer & Carr, 2003). The bottom line for suppliers is that they have been found to be less likely to make investments in relationships using online auctions and that their perceptions of buyers taking advantage of them (opportunism) increases (Jap, 2003). In addition, the suppliers feel that auctions have the effect of making their products a commodity, reducing the decision to only price (Jap, 2002). Thus it is apparent that procurement auction design must take into account these three factors: increasing the likelihood of supplier long-term investment, assuaging fears of buyer opportunism, and moving the auction away from focusing only on price. In the following sections, we suggest improvements to basic auction design that attack these three limitations.

4. Determining winner in a multiattribute RFQ when price is not the only criterion In the foregoing industrial procurement scenario, auctions are dominated by price-only considerations. Buyers procure low cost standardized items using the online reverse auction mechanism and high value complex items via the offline traditional RFQ process. An RFQ is evaluated using multiple non-price attributes like quality of the delivered materials, delivery time, contract terms, reputation of the

suppliers and incumbent switching costs. The buyer can have differential preference on such choice criteria and suppliers can have certain specialized dimensions on which they want to compete. In real life, it is unlikely there would be a clear choice among the bidders who can exceed others in all the criteria for selection. In this section, we propose a method that can address a buyer’s preferences for various bid attributes—including relational variables—that can be used in a multi-attribute reverse auction to determine the winning bidder. 4.1. A quantitative method for evaluating bids Studies on supplier evaluation and selection have gained significant importance in marketing literature. For example, Weber and Desai (1996) propose a parallel coordinate graphical representation of supplier efficiency on multiple criteria and visual identification of benchmarked values to negotiate with inefficient vendors. Talluri and Ragatz (2004) suggest an analytic hierarchical process based method of supplier selection in case of multi-criteria bid. Beil and Wein (2003) propose an inverse optimization based auction mechanism where the buyer ranks bids in terms of price and other non-price attributes with an objective to maximize buyer’s utility. In this study, we propose the method of Data Envelopment Analysis (DEA) (Charnes, Cooper, & Rhodes, 1978) to analyze the importance of the multiple-choice criteria, analyze supplier performance on the choice set and finally, determine the winner whom we call the efficient supplier. We define efficiency of a supplier by the ratio of the amount of resources (inputs) like price, quality, and delivery time the supplier requires to deliver one unit of item (output). The concept of relative efficiency is introduced by considering a supplier efficient if there exists no other supplier who can deliver equal quantity of items using same or less resource (like less price, less rejections, less tardiness). A supplier is inefficient if there is another supplier or a group of suppliers who can provide the same quantity of items with less of at least one resource. Zhu (2004) proposes that the buyer–seller game model of Talluri (2002) for evaluation of alternative bids can be simplified using DEA and performance of bidders can be measured against the best practice frontier. To illustrate, below we use an abridged version of this methodology for supplier selection. We explain here the concept of supplier efficiency using DEA in the case of a two attribute RFQ via a graphical representation. Consider in an online reverse auction, the buyer has specified two criteria in the RFQ, namely price per unit of the item and the quality of the product (measured by percentage of rejection of items). Five suppliers have bid for the contract. We represent their bids in a two-dimensional scatter graph (see Fig. 1). Clearly supplier 2 is dominating suppliers 3 and 4 as for same level of quality of items both suppliers 3 and 4 are quoting higher price per

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time (percentage of on time delivery); and quality (percentage of items which are accepted). The buyer is implementing JIT manufacturing. Therefore, the suppliers’ willingness to collaborate with the buyer’s requirements is also taken as a criterion for selection, measured with a five point Likert scale (5 = very willing, 1= not willing). Table 2 shows bid data for one unit of item to be procured for six suppliers on the chosen criteria. It is quite evident that there is no clear winner. Since it involves more than two attributes, we use the optimization model of DEA to identify the winner (the mathematical explanation of the model is given in Appendix A). So, we have three inputs (price, on-time delivery and quality of items) and two outputs (willingness to collaborate and number of units of items to be supplied) in our model. DEA uses a linear optimization model where it minimizes (or maximizes according to the model used) the ratio between the set of weighted outputs that a buyer looks for from the suppliers and the weighted set of inputs which suppliers provide. This is defined as the efficiency of suppliers in their bid process. For example, in this illustration, the buyer is interested to obtain the units of item from the suppliers and their willingness to collaborate in the long run. So, these are the set of outputs. The suppliers provide purchase price, on time delivery and quality, which are the set of inputs to the system. The weights are not pre-fixed but are decided in such a fashion that for each supplier this ratio reaches the maximum. Thus, each supplier is given the maximum advantage so that their inherent strengths are given due weighting in winner selection. Efficiency of each supplier is obtained by comparing that firm with the other suppliers with an objective of benchmarking. DEA finds the set of suppliers who can provide better efficiency with the same set of inputs and outputs as any particular supplier can provide. From Table 2, it is evident that suppliers S2, S4 and S5 are efficient, whereas S1, S3 and S6 are inefficient. Also, S5 is the benchmarked firm for all the inefficient suppliers and is referred highest number of times. Thus, S5 is the clear winner and the buyer can award the contract to S5.

Fig. 1. DEA efficiency frontier.

unit. However, it is not so clear whether supplier 2 has quoted a better bid than supplier 5 or vice versa. This issue is resolved by connecting, in a piecewise linear fashion, all the suppliers where (1) no other supplier has a lower price (to their left) and a lower or equivalent rejection rate (below them) and (2) a lower rejection rate and a lower or equivalent price. Hence, we get the efficiency frontier which envelopes the data. Suppliers (2 and 5) who are on the frontier are defined to be efficient and others are inefficient (1, 3 and 4). Although supplier 1 is on the efficiency frontier, it has a higher rejection rate than supplier 2 given their price quoted are same. The efficient projection of supplier 3 on the frontier is 3V and it is referring firm 2 and 5 as its peers, which are efficient themselves. Thus supplier 2 and 5 form the reference set for supplier 3. Supplier 1 is 50% efficient with supplier 2 is its peer and its projection on the frontier is 1V. Point 1V lying on the frontier parallel to the axis is not efficient as it can further reduce its input usage to the level of supplier 2. So, supplier 1 is said to be radially inefficient. This illustrates how the DEA methodology works to find out relative efficiency of a group of homogeneous suppliers using multiple inputs to generate multiple outputs and find out the winner. 4.2. An empirical illustration

5. Variations on the auction format A buyer who wishes to procure an item through reverse auction has decided on three criteria for supplier selection. These are price (purchase price per unit of item); delivery

Once the supply chain manager has decided on a basic design, has included relational variables in the RFQ, and is

Table 2 Bid data on multi-criteria RFQa Supplier

Price ($US)/unit

% Accepted

% On-time delivery

Willingness to collaborate

Number of items

Efficiency (%)

Benchmarks

S1 S2 S3 S4 S5 S6

0.1958 0.1881 0.2204 0.2081 0.2118 0.2996

98.8 99.2 100.0 97.9 97.7 98.8

95.0 93.0 100.0 100.0 97.0 96.0

3 5 3 5 5 2

1 1 1 1 1 1

99.92 100 97.7 100 100 99.46

S2, S4, S5

a

A portion of the bid data is derived from a study conducted by Weber and Desai (1996).

S5

S2, S5

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Table 3 Advanced procurement auction design Design element

Key factors in applying method

Intended relational target

Positive outcomes

Possible negative outcomes

When to apply

Subsidies to partner firms

Cost estimate precision Scale of cost disadvantage

Promoting investment by particular partner firms

Increased transaction specific investments

Getting subsidy amount wrong

Price/contract re-negotiations

Cost of renegotiation Number of firms involved Bid preparation costs Ceiling price (cost of production for marginal sellers)

Avoiding seller’s winner’s curse Promoting buyer’s reputation Keeping buyer’s reputation intact among a limited number of sellers

Lowers uncertainty and makes risks clearer to both parties Enabling potential partners to continue as part of available supply chain

Potential for increasing costs for both parties

Large and long-term investments are necessary for efficient operations High cost, high risk, and more uncertain projects Low number of incumbents and limited potential new entrants

Payments to losing bidders

ready to apply the DEA methodology for multi-attribute bid evaluation, it may still be possible to even further strengthen the relational character of the auction. Based on some recent advances in auction design, in this section we are suggesting a number of potential variations on the basic auction format for further mitigation of relationship-based problems (see Table 3 for summary). 5.1. Subsidies for investment It has been suggested that subsidies be granted for relationship specific investments (Menezes, Pitchford, & Wait, 2003). The logic is that if a supplier is not assured of future contracts, that firm is less likely to engage in investments that are dedicated to the procuring company’s projects, products, or processes. Thus, incumbent suppliers need to be given sufficient incentive to make long-term investments that will make the final product cheaper or superior in quality. Rothkopf, Harstad, and Fu (2003) have more fully explored the concept of subsidizing certain bidders. In their conceptualization, a particular class of bidders is seen as operating at an economic disadvantage. While they were focusing on positive affirmative action (veterans, racial minorities, women, or small business), they explicitly state that their concept stems from the notions of potential economic disadvantage, past service, and fairness. In the supply chain world, each of these latter three categories makes sense: extra considerations for long-term relation-specific investments, past contributions to the collective good, and buyer fairness to existing suppliers. Thus, we are proposing a direct subsidy to long-term relational partners who are forced to bear costs that are not directly tied to the immediate contract in question, but are longer-term in nature, such as warranty services or research and development expenditures. For the moment, let us ignore the qualitative issues of past contributions and fairness and focus on economic benefits, as Rothkopf et al. (2003) suggest. Using relatively simple and well-documented theory from the auction literature, they build a model of expected procurement costs as a function of the relative cost disadvantage of one

Potential for unnecessary opportunistic payments

participant. The driving factor in this model is that in order to maximize the advantaged bidding firms’ profits, firms must bid more aggressively in the face of the subsidized competition. In their example of only two competitors in the auction, it turns out that the optimal subsidy ranges from 54% to 72% of subsidizing the entire cost disadvantage.4 Expected procurement costs drop substantially, depending on the degree to which the cost structure is understood and the amount of cost disadvantage. Let us put these two factors in supply chain terms. First, when a buyer understands the cost structure of the industry in great detail (e.g., the types of investments needed, the level of effort required to develop new products and processes, or the costs of managing these investments), it means that the buying firm should subsidize the incumbents for their past investments more aggressively. At a precision factor of 12% (a narrow range of uncertainty for total costs), the optimal percentage of total subsidy is about 70% of the cost disadvantage. When the uncertainty factor rises to 52%, the optimal subsidy is about 60% of the cost disadvantage. As for the degree of cost disadvantage (i.e., increasing past, present, and future relation specific investments), increasing disadvantage lowers the optimal subsidy by about 5–8%. Naturally, the cost reduction is very small when the cost disadvantage is small; at 5% disadvantage, the reduction in procurement costs is under 1%. Yet when the disadvantage grows large, to 50% or more, the savings can be quite dramatic, 10%, 15%, even 25%, and that is including the cost of the subsidy! Perhaps the best news is that the estimates of the required subsidy are in a relatively tight band and do not require extremely accurate estimates of uncertainty and cost disadvantage. Therefore, when there are very high investments required, leading to very significant short-run cost disadvantages, the use of subsidies is very much favored as an auction design strategy.

4 While the outcomes shown here are for two bidders with one competitor being subsidized, Rothkopf et al. (2003) present results for various scenarios for different numbers of competitive bidders. The results do not vary markedly from these percentages.

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5.2. Price negotiation after auction One of the primary purposes of using auctions is to discover price where there is no established market. The basic assumption of the auction is that the buyer does not know the costs of the various sellers—and neither do the sellers know the value the buyer places on the goods at auction. The uncertainty factor extends even to the sellers; while they may understand the costs of an existing product line quite well (with a factor for future variations in raw materials, labor, etc.), they may only be estimating the cost of a new product or service. Therefore, a key feature of bidding in auctions is the winner’s curse, where the winning bidder is unable to judge the true cost of an item and bids in excess to win the auction (Klemperer, 1999). In the supply chain, this means an entirely new set of risks for the sellers: the firm that makes the most optimistic estimate in the face of common uncertainty is the firm which is most likely to win the bidding—and in turn is most likely to fail in producing the product profitably in response to the bid. Wang (2000) suggests that negotiations after the auction represent a way for procurers to obtain more information about the winning firm’s costs. We go beyond Wang’s argument and say that the negotiations can and should go both ways. That is, not only do negotiations provide the opportunity for the procurer to learn more about the selling firm and it’s costs, but also the seller has the opportunity to learn more about project costs based on the knowledge and expectations of the buyer. In Wang’s (2000) model, the procuring firm has the option to not accept the final price (like a reserve or ceiling price), accept the bid, or offer negotiations. The importance of having the three options is that the possibility of having the bid accepted keeps selling firms from artificially bidding too low merely to get the bid, then renegotiate the price upward. The auction price therefore becomes the opening price in further negotiations between the buyer and the winning bidder. These negotiations do have costs in terms of time, money, and managerial energy and focus. Thus, the auction serves to narrow the field from the total number of potential firms to one that is likely to have the lowest costs and with which the procurer can investigate more deeply true costs. The major factors that determine whether firms should negotiate are the costs and benefits of negotiation. Naturally, when negotiation costs are high or the project costs are low, negotiation is less favored. Interestingly, Wang (2000) also points out that as the number of bidding firms increases, negotiations are less likely to produce benefits. That is, the competitive pressure of more selling firms causes each firm to engage in more aggressive bidding, making the gains from additional negotiations proportionally smaller—even when each selling firm may have uncertainty about the true costs of the project. In fact, Wang (2000) goes even further, saying that when the submitted bids are vastly separated (often caused by a very low bidder), negotiation is more likely. We argue that

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widely separated bid prices may be a good indicator of uncertainty in understanding the true cost of the project— and therefore the risk involved. Because the winner’s curse would be more likely in riskier situations, where the actual costs of developing and producing a product are unknown, buying firms may have a short-term incentive to auction rather than negotiate. However, procuring firms that aggressively bid out many components and materials might come to find that they begin to develop a reputation for creating winner’s curse blosersQ out of their auctions and ultimately cannot attract new bidders for their auctions. Therefore, from a relational viewpoint, it also makes sense for the procuring firm to undertake negotiations with the winning bidder to understand the future project costs more clearly. The downside potential for adding post-auction negotiations is increased procurement costs. Overall, Wang compares the costs from auctions with and without negotiations and finds that prices in procurement auctions with negotiations should be very weakly higher than those without negotiations and that the cost of negotiating is incurred to both parties. But Wang (2000) emphasizes the very small cost in the model; we believe the tiny negative is worth the relational gains. 5.3. Payments to losing bidders The new ideas contained so far in the present paper clearly take care of the bwinnersQ, past, present, and even future. But what of the procurement blosersQ? Once they have lost, will they be forever banished from the bpromised landQ of winners? Recall that auctions succeed in environments with numerous bidders; not only those firms in the particular industry, but also potential entrants. To maintain efficient procurement auctions, the losing bidders must be kept interested and able to compete for future auctions. Numerous industries have a limited number of incumbents and a limited number of potential entrants. One example is military procurement. Major projects, such as aircraft, ships, and tanks come up rarely and are enormous in scale, limiting the number of potential entrants and making bid preparation itself quite expensive. To increase the number of bidders in such a situation, Colombo suggests making payments to losing bidders (2003). The payment entices firms who have only marginal chances of winning to enter the bidding. His model, based on the buying firm setting a ceiling price (a reserve price), a prize that is dependent on the number of firms that bid, and that the sale does not go through if only one seller bids. The model suggests that the appropriate reward consists of offering to pay the expected price of the cost of bid preparation. Below this amount, the odds of being unable to procure the good rise dramatically. Even better, his model suggests that paying such an award does not increase the expected cost of procurement for the buyer! That is, the size of the payment has no impact on the procurement costs.

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Intuitively, this occurs because the larger subsidy attracts more marginal bidders and drives down the price. From a relational viewpoint, such a structured approach can make the suppliers exit the auction process with a renewed sense of respect for the buyer rather than feeling as if they have been left adrift without contact or hope for the future in the supply chain environment. However, there is the possibility of unnecessary award payments. Colombo (2003) points out that sellers who enter the bidding primarily to capture the subsidy cannot enter a bid if they do not have a cost of producing the item equal to or lower than the sum of their cost of production minus the subsidy, limiting the potential opportunism.

6. Discussion Procurement auctions require many decisions of form, structure, and design intended to attract more bidders and lower the price paid. Yet, the very same bidders have the potential to destroy the existing long-term relationships that promoted strategic investments in research and development, plant capacity and equipment, service arrangements, etc. We have suggested that it is possible to design auctions more conducive to long-term investments and relationships by subsidizing relational partners, making payments for losing bidders, even re-negotiating final contract bid prices and specifications. In addition to bid price and endogenous attributes such as quality and delivery time, exogenous attributes can also be incorporated in bid evaluation, such as supplier’s willingness to collaborate with the buyer on a long-term basis, make investments with a long-term strategic view, supplier reputation, or past history with the buyer. The weights used to evaluate the criteria are chosen for individual suppliers so that the performance of each is shown in the best possible manner and maximize their own efficiency score relative to the other suppliers being considered. It resembles the real life bidding process very closely as the scoring rule for supplier selection and buyer’s preference keep changing with time. The buyer may wish to give due weighting to individual attributes such that the expertise of individual suppliers can be properly utilized. Yet, a new tension is created: balancing the need to support incumbents, promote long-term thinking, and investment within the supply chain/marketing channel with the need to attract new competitive bidders from outside the existing core group of suppliers. Will such new arrangements be seen as unfairly favoring existing suppliers? Will incentives in future contracts make it a bwinner-take-allQ auction that new bidders will only want to engage in once in a great while? This new problem led to the idea of potentially dividing payments across both winners and losers in the bidding process. In the end, the central concept of our design ideas is to avoid the perception of opportunism by appearing fair to both the incumbents and the potential new suppliers.

This fairness is a clear benefit to the procuring firm. Empirical evidence suggests there is a significant impact of reputation on auction outcomes like price (Ba & Pavlou, 2002; Dewan & Hsu, 2001) and probability of sale (Resnick & Zeckhauser, 2000). Bajari and Hortacsu (2000) find that auction outcomes increase with reputation and the trust associated and presence of negative comments decrease outcomes. Moberg and Speh (2003) found that bquestionableQ business practices (such as tactics in negotiation and price changes) have a severe impact on the strength of supply chain relationships. Powerful procuring firms find that their relationship quality (conflict, trust, and commitment) with more vulnerable partner firms declines as they are seen to be less fair to their partner firms (Kumar, Scheer, & Steenkamp, 1995). Unfortunately, even some of the suggested ideas in the foregoing sections might be construed in some way by partner firms as possibly being manipulated: barbitraryQ measures of relational commitment used in the bid evaluation process, bdemandingQ re-negotiations, or bunfairQ subsidies to incumbents. Haake, Raith, and Su, (2002) talk about fairness in both constructive (i.e., monetary) and procedural (i.e., process of division) senses. The basic openness and transparency of auctions is the quintessential essence of fairness because they operate via a clear set of rules. When done in combination with the relational practices outlined in this paper (bleveling the playing fieldQ), procurement auctions have economic fairness as well. In fact, some have argued that auctions provide legitimacy in a unique way that is irreproducible (Rothkopf & Harstad, 1994). Some might ask whether we have come full circle. That is, has the auction become so loaded down with add-ons that firms might as well return back to direct negotiation? Wasn’t the point to achieve lower prices with a simple process (the auction)? Aren’t buyers bgiving backQ margin via side payments, incentives, and so forth? The good news is that the available literature seems to indicate that many of the concepts suggested in this paper will have a very low cost, even savings in some circumstances. Thus, we argue that relationship-friendly auctions remain a viable option for many, if not all, major supply chain purchases.

7. Future directions As with any set of new theoretical applications, there is the need to try out the ideas in practice. While our recommendations are supported by sound theory, insight, and empirical evidence from various auction formats, the bproof is in the puddingQ, as they say. Our intent has been to show the way forward with new auction designs; firms willing to experiment with these new forms may experience the best of both worlds: enhanced long-term relationships and the flexibility/pricing benefits of auctions. The work of Jap (2003) is a wonderful example of

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what can be accomplished with auctions in an experimental mode. There is also room in the future for additional broadbased surveys of actual practice. Beam and Segev (2004) surveyed 100 online auction sites to identify current practices, trends and new business models. Lucking-Reiley (2000) surveyed 142 different online auction sites to identify the business practices followed by these sites, goods offered for sale and their auction mechanisms. Such survey-based studies have brought up issues like how such Internet based auction institutions pose questions for the traditional economic auction theory. Certainly in a fastmoving area such as online auctions, it is time again for such a study. That work would almost certainly answer some questions, create new possibilities, and spur new lines of research.

Acknowledgements The authors wish to acknowledge the gracious interest and encouragement of the special issue editor, Richard Lancioni, for his guidance in forming the direction of this paper.

Appendix A. Constant return to scale DEA model Min H Subject to X kj xij VHxi0 i ¼ 1; 2; N ; m j X kj yrj zyr0 r ¼ 1; 2; N ; s j

kj z0

ja1; 2; N ; n

where x ij and y rj are the amount of ith input and rth output generated by the jth supplier. m is the number of inputs, s is the number of outputs and n is the number of suppliers. In our example, n=6, m=3 (price, delivery time and quality), s=2 (willingness to collaborate and number of items to be supplied).

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tiation tools for buyers. European Journal of Operational Research, 90(1), 142 – 155. Zhu, J. (2004). A buyer–seller game model for selection and negotiation of purchasing bids: Extensions and new models. European Journal of Operational Research, 154, 150 – 156. Shawn P. Daly is Associate Professor of Marketing at Tiffin University in Tiffin, Ohio, USA, where he also serves as Dean of Graduate and Online Education. His research interests include international business, marketing channels behavior, e-logistics, and marketing pedagogy. Dean Daly’s work has been published in a wide variety of venues including Industrial Marketing Management, the Journal of Marketing Education, and the Proceedings of the American Marketing Association Educator’s Conferences. Prithwiraj Nath is Assistant Professor of Marketing at the Xavier Labour Relations Institute in Jamshedpur, India. His research area includes impact of marketing on the financials, marketing performance measurement and benchmarking, marketing decision models and forecasting box-office success for the movie industry. Prof. Nath has published several research papers in the top grade international journals in the area of Marketing and Operations Management.