Behavior in Multi‐Unit Demand Auctions: Experiments with Uniform Price and Dynamic Vickrey Auctions

We experimentally investigate the sensitivity of bidders demanding multiple units of a homogeneous commodity to the demand reduction incentives inherent in uniform price auctions. There is substantial demand reduction in both sealed bid and ascending price clock auctions with feedback regarding rivals' drop‐out prices. Although both auctions have the same normal form representation, bidding is much closer to equilibrium in the ascending price auctions. We explore the behavioral process underlying these differences along with dynamic Vickrey auctions designed to eliminate the inefficiencies resulting from demand reduction in the uniform price auctions.     

A Study of Online Auction Sellers’ Intention to Switch Platform: The Case of Yahoo!Kimo Versus Ruten_eBay*

The study of consumers’ switching from one service provider to another has a long tradition in economics, information systems, and marketing. The emergence of electronic commerce presents new challenges in understanding consumers’ switching intentions in the context of e‐commerce in general and online auctions in particular. With the abundance of literature on online auctions, there is a surprising lack of research on auction sellers’ intentions to switch from one online auction platform to another. Using the competition between Yahoo!Kimo and Ruten_eBay, two leading auction platforms in Taiwan, as the backdrop, we developed a research model and collected empirical data based on this real case to study what factors influence auction sellers to switch to a competing service provider. We find that the higher the procedural switching costs, financial switching costs, relational switching costs, site design quality, or interaction quality, the lower the intention of an auction seller to switch to a competing auction service provider. A higher perceived transaction fee, however, leads to a higher switching intention.     

Split‐Award Procurement Auctions—Can Bayesian Equilibrium Strategies Predict Human Bidding Behavior in Multi‐Object Auctions?

We analyze if and when symmetric Bayes Nash equilibrium predictions can explain human bidding behavior in multi‐object auctions. We focus on two sealed‐bid split‐award auctions with ex ante split decisions as they can be regularly found in procurement practice. These auction formats are straightforward multi‐object extensions of the first‐price sealed‐bid auction. We derive the risk‐neutral symmetric Bayes Nash equilibrium strategies and find that, although the two auction mechanisms yield the same expected costs to the buyer, other aspects of the two models, including the equilibrium bidding strategies, differ significantly. The strategic considerations in these auction formats are more involved than in single‐lot first‐price sealed‐bid auctions, and it is questionable whether expected utility maximization can explain human bidding behavior in such multi‐object auctions. Therefore, we analyzed the predictive accuracy of our equilibrium strategies in the laboratory. In human subject experiments we found underbidding, which is in line with earlier experiments on single‐lot first‐price sealed‐bid auctions. To control for regret, we organize experiments against computerized bidders, who play the equilibrium strategy. In computerized experiments where bid functions are only used in a single auction, we found significant underbidding on low‐cost draws. In experiments where the bid function is reused in 100 auctions, we could also control effectively for risk aversion, and there is no significant difference of the average bidding behavior and the risk‐neutral Bayes Nash equilibrium bid function. The results suggest that strategic complexity does not serve as an explanation for underbidding in split‐award procurement auctions, but risk aversion does have a significant impact.     

An Analysis of Scoring and Buyer‐Determined Procurement Auctions

In procurement auctions, the object for sale is a contract, bidders are suppliers, and the bid taker is a buyer. The suppliers bidding for the contract are usually the current supplier (the incumbent) and a group of potential new suppliers (the entrants). As the buyer has an ongoing relationship with the incumbent, he needs to adjust the bids of the entrants to include non‐price attributes, such as the switching costs. The buyer can run a scoring auction, in which suppliers compete on the adjusted bids or scores, or, he can run a buyer‐determined auction, in which suppliers compete on the price, and the buyer adjusts a certain number of the bids with the non‐price attributes after the auction to determine the winner. Unless the incumbent has a significant cost advantage over the entrants, I find that the scoring auction yields a lower average cost for the buyer, if the non‐price attributes are available. If the non‐price attributes are difficult or expensive to obtain, the buyer could run a buyer‐determined auction adjusting only the lowest price bid.     

Introduction to the Special Issue on E‐Auctions for Procurement Operations

We present nine papers on e‐auctions for procurement operations. The first of the issue's three sections is a foreword highlighting current trends and future research directions. The second section is composed of three commissioned survey papers, covering auctions within e‐sourcing events, electronic markets for truckload transportation, and games and mechanism design in machine scheduling. The final section consists of five research papers on the following topics: whether auction parameters affect buyer surplus in e‐auctions for procurement, collusion in second‐price auctions under minimax regret, the effect of timing on jump bidding in ascending auctions, market‐based allocation with indivisible bids, and ranked items auctions and online advertisement.     

Do Auction Parameters Affect Buyer Surplus in E‐Auctions for Procurement?

Although the initial euphoria about Internet‐enabled reverse auctions has given way to a cautious but widespread use of reverse auctions in business‐to‐business (B2B) procurement, there is a limited understanding of the effect of auction design parameters on buyer surplus. In this paper, we study the effect of bidding competition, information asymmetry, reserve price, bid decrement, auction duration, and bidder type on buyer surplus. We collected field data on more than 700 online procurement auctions conducted by a leading auctioneer and involving procurement items worth millions of dollars. Consistent with the predictions of auction theory, the results indicate that bidding competition, reserve price, and information sharing affect buyer surplus. Unlike previous findings in the consumer‐to‐consumer context, we find that bid decrement and auction duration have no effect in B2B procurement auctions. Our results suggest that use of the rank‐bidding format increases buyer surplus when incumbent suppliers participate in the auction. We discuss the theoretical and managerial implications of these findings for future research and for optimal design of online procurement auctions.     

Auctions within E‐Sourcing Events

The paper presents a survey of current industry practices in designing and running auctions as part of e‐sourcing events. We report our findings from numerous interviews with auction makers in leading e‐sourcing application vendors. The differences between auction theory and auction practice pose a number of interesting and important research questions for the Operations Management community; we conclude with a discussion of lessons learned and open research questions.     

Collusion in Second‐Price Auctions under Minimax Regret Criterion

Collusion in auctions, with different assumptions on distributions of bidders' private valuation, has been studied extensively over the years. With the recent development of on‐line markets, auctions are becoming an increasingly popular procurement method. The emergence of Internet marketplaces makes auction participation much easier and more convenient, since no physical presence of bidders is required. In addition, bidders in on‐line auctions can easily switch their identities. Thus, it may very well happen that the bidders in an auction have very little, if any, prior knowledge about the distributions of other bidders' valuations. We are proposing an efficient distribution of collusive profit for second‐price sealed bid auctions in such an environment. Unlike some known mechanism, which balance the budget only in expectation, our approach (which we call Random k) balances the budget ex‐post. While truth‐telling is not a dominant strategy for Random k, it is a minimax regret equilibrium.     

Ranked Items Auctions and Online Advertisement

We study auctions for a set of commonly‐ranked items where each buyer has unit demand. This setting has promising applications in areas such as keyword auctions in the search engine advertising industry, the sale of quality‐ranked raw materials, etc. An auction mechanism suitable for this setting is the simultaneous pooled auction (SPA), where each bidder simultaneously submits a single bid and is allocated an object based on the rank of his bid among all the bids. We study how to improve the seller's expected revenue by enforcing a reserve price in an SPA. We find that the use of an appropriate reserve price may significantly increase the seller's revenue, especially when the number of items for sale is relatively large compared to the number of participating bidders. One inherent problem in the SPA is that some bidders may incur ex post losses; that is, they pay more than what they value the received objects. We propose a tailored VCG mechanism that generates the same expected revenue as the SPA does, while bidders do not incur any ex post loss. We also discuss the potential applications of this research to keyword auctions.     

Optimal Design of the Online Auction Channel: Analytical,Empirical, and Computational Insights*

The focus of this study is on business‐to‐consumer (B2C) online auctions made possible by the advent of electronic commerce over an open‐source, ubiquitous Internet Protocol (IP) computer network. This work presents an analytical model that characterizes the revenue generation process for a popular B2C online auction, namely, Yankee auctions. Such auctions sell multiple identical units of a good to multiple buyers using an ascending and open auction mechanism. The methodologies used to validate the analytical model range from empirical analysis to simulation. A key contribution of this study is the design of a partitioning scheme of the discrete valuation space of the bidders such that equilibrium points with higher revenue structures become identifiable and feasible. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of key control factors such as the bid increment, resulting in substantial losses in a market with already tight margins. With this in mind, we put forward a portfolio of tools, varying in their level of abstraction and information intensity requirements, which help auctioneers maximize their revenues.     

The Affiliation Effect in First‐Price Auctions

We study the monotonicity of the equilibrium bid with respect to the number of bidders n in affiliated private‐value models of first‐price sealed‐bid auctions and prove the existence of a large class of such models in which the equilibrium bid function is not increasing in n. We moreover decompose the effect of a change in n on the bid level into a competition effect and an affiliation effect. The latter suggests to the winner of the auction that competition is less intense than she had thought before the auction. Since the affiliation effect can occur in both private‐ and common‐value models, a negative relationship between the bid level and n does not allow one to distinguish between the two models and is also not necessarily (only) due to bidders taking account of the winner's curse.     

Keyword Auctions,Unit‐Price Contracts,and the Role of Commitment

Motivated by the enormous growth of keyword advertising, this paper explores the design of performance‐based unit‐price contract auctions, in which bidders bid their unit prices and the winner is chosen based on both their bids and performance levels. The previous literature on unit‐price contract auctions usually considers a static case where bidders' performance levels are fixed. This paper studies a dynamic setting in which bidders with a low performance level can improve their performance at a certain cost. We examine the effect of the performance‐based allocation on overall bidder performance, auction efficiency, and the auctioneer's revenue, and derive the revenue‐maximizing and efficient policies accordingly. Moreover, the possible upgrade in bidders' performance level gives the auctioneer an incentive to modify the auction rules over time, as is confirmed by the practice of Yahoo! and Google. We thus compare the auctioneer's revenue‐maximizing policies when she is fully committed to the auction rule and when she is not, and show that the auctioneer should give less preferential treatment to low‐performance bidders when she is fully committed.     

Level‐k Auctions: Can a Nonequilibrium Model of Strategic Thinking Explain the Winner's Curse and Overbidding in Private‐Value Auctions?

This paper proposes a structural nonequilibrium model of initial responses to incomplete‐information games based on “level‐k” thinking, which describes behavior in many experiments with complete‐information games. We derive the model's implications in first‐ and second‐price auctions with general information structures, compare them to equilibrium and Eyster and Rabin's (2005) “cursed equilibrium,” and evaluate the model's potential to explain nonequilibrium bidding in auction experiments. The level‐k model generalizes many insights from equilibrium auction theory. It allows a unified explanation of the winner's curse in common‐value auctions and overbidding in those independent‐private‐value auctions without the uniform value distributions used in most experiments.     

Designing Multi‐Attribute Procurement Mechanisms for Assortment Planning

This research investigates how to design procurement mechanisms for assortment planning. We consider that a retailer buys directly from a manufacturer who possesses private information about the per‐unit variable cost and per‐variety setup cost. We first develop a screening model to assist the retailer in integrating assortment planning into supply chain contracting processes when only one manufacturer is available. We demonstrate that the screening mechanism is optimal among all feasible procurement strategies. When there are multiple competing manufacturers, we propose a supply contract auctioning mechanism and evaluate its performance. In this mechanism, the retailer announces a contract menu and the manufacturer that bids the highest upfront fee paid to the retailer wins the auction. The winner then chooses and executes a contract from the contract menu. We show that when the retailer uses the optimal screening contract menu as the object of the auction, it achieves the optimal procurement outcome if the screening contract menu does not pay rent to any manufacturer type. This finding sheds light on the connection between screening and auction mechanisms when there exists multi‐dimensional private information.     

Viability of Auction‐Based Revenue Management in Sequential Markets

The Internet is providing an opportunity to revenue management practitioners to exploit the potential of auctions as a new price distribution channel. We develop a stochastic model for a high‐level abstraction of a revenue management system (RMS) that allows us to understand the potential of incorporating auctions in revenue management in the presence of forecast errors associated with key parameters. Our abstraction is for an environment where two market segments book in sequence and revenue management approaches consider auctions in none, one, or both segments. Key insights from our robust results are (i) limited auctions are best employed closest to the final sale date, (ii) counterbalancing forecast errors associated with overall traffic intensity and the proportion of customer arrivals in a segment is more important if an auction is adopted in that segment, and (iii) it is critically important not to err on the side of overestimating market willingness to pay.     

REALIZATION OF THE VIRTUAL ENTERPRISE PARADIGM IN THE CLOTHING INDUSTRY THROUGH E‐BUSINESS TECHNOLOGY

This paper presents a methodology and a case study for supply chain management in the clothing industry that makes extensive use of the virtual enterprise paradigm. The main research goal was to design and implement a prototype e‐business software component and carry out tests in several industrial users. The research effort resulted in the extended production data management system (epms), which supported the business processes of customer order management, subcontractor selection, and multi‐site/multi‐firm production orders release. The enablers of this software application were business‐to‐business (b2b) e‐commerce technologies in the operating context of application service providers (asps).     

Reputation and Mechanism Choice in Procurement Auctions: An Experiment

We experimentally study the role of reputation in procurement using two common mechanisms: price‐based and buyer‐determined auctions. While buyers are bound to buy from the lowest bidder in price‐based auctions, they can choose between bidders in buyer‐determined auctions. Only the latter buyers can consider the reputation of bidders. We find that bidders supply higher quality in buyer‐determined auctions leading to higher market efficiencies in these auctions. Accordingly, buyers prefer the buyer‐determined auction over the price‐based auction, while only half of the bidders do so. A more detailed analysis of buyers' and bidders' behavior and profits provides insights into their mechanism choice.     

Integrated Procurement Planning in Multi‐division Firms

For large multi‐division firms, coordinating procurement policies across multiple divisions to leverage volume discounts from suppliers based on firm‐wide purchasing power can yield millions of dollars of savings in procurement costs. Coordinated procurement entails deciding which suppliers to use to meet each division's purchasing needs and sourcing preferences so as to minimize overall purchasing, logistics, and operational costs. Motivated by this tactical procurement planning problem facing a large industrial products manufacturer, we propose an integrated optimization model that simultaneously considers both firm‐wide volume discounts and divisional ordering and inventory costs. To effectively solve this large‐scale integer program, we develop and apply a tailored solution approach that exploits the problem structure to generate tight bounds. We identify several classes of valid inequalities to strengthen the linear programming relaxation, establish polyhedral properties of these inequalities, and develop both a cutting‐plane method and a sequential rounding heuristic procedure. Extensive computational tests for realistic problems demonstrate that our integrated sourcing model and solution method are effective and can provide significant economic benefits. The integrated approach yields average savings of 7.5% in total procurement costs compared to autonomous divisional policies, and our algorithm generates near‐optimal solutions (within 0.75% of optimality) within reasonable computational time.     

Replenishment Policies for Multi‐Product Stochastic Inventory Systems with Correlated Demand and Joint‐Replenishment Costs

This study analyzes optimal replenishment policies that minimize expected discounted cost of multi‐product stochastic inventory systems. The distinguishing feature of the multi‐product inventory system that we analyze is the existence of correlated demand and joint‐replenishment costs across multiple products. Our objective is to understand the structure of the optimal policy and use this structure to construct a heuristic method that can solve problems set in real‐world sizes/dimensions. Using an MDP formulation we first compute the optimal policy. The optimal policy can only be computed for problems with a small number of product types due to the curse of dimensionality. Hence, using the insight gained from the optimal policy, we propose a class of policies that captures the impact of demand correlation on the structure of the optimal policy. We call this class (s, c, d, S)‐policies, and also develop an algorithm to compute good policies in this class, for large multi‐product problems. Finally using an exhaustive set of computational examples we show that policies in this class very closely approximate the optimal policy and can outperform policies analyzed in prior literature which assume independent demand. We have also included examples that illustrate performance under the average cost objective.     

Market‐Based Allocation with Indivisible Bids

We study multiunit double auctions accepting bids with indivisibility constraints. Modeling the auction problem as a Multiple Choice Knapsack Problem and using dynamic programming, we show that incremental computations during bid processing can speed the handling of key auction operations such as clearing and quoting. We propose different price‐quote policies and study their influence on the efficiency of market‐based allocation. Using a reconfigurable manufacturing scenario where agents trade large quantities of multiple goods, we demonstrate potential benefits of supporting indivisibility constraints in bidding. These benefits are highly sensitive to the form of price quote provided, indicating interesting tradeoffs in communication and allocation efficiency.