Designing Intelligent Software Agents for B2B Sequential Dutch Auctions: A Structural Econometric Approach

We study multi-unit sequential Dutch auctions in a complex B2B context. Using a large real-world dataset, we apply structural econometric analysis to recover the parameters governing the distribution of bidders’ valuations. The identification of these parameters allows us to simulate auction results under different designs and perform policy counterfactuals. We also develop a dynamic optimization approach to guide the setting of key auction parameters. Given the bounded rationality of human decision makers, we propose to augment auctioneers’ capabilities with high performance decision support tools in the form of software agents. Our paper contributes to both theory and practice of auction design. From the theoretical perspective, this is the first study that explicitly models the sequential aspects of Dutch auctions using structural econometric analysis. From the managerial perspective, this paper offers useful implications to business practitioners for complex decision making in B2B auctions

Essays on Information Flows and Auction Outcomes in Business-to-Business Market: Theoretical and Empirical Evidence

In this dissertation, I have three separate essays in the context of Business-to Business (B2B) auctions; in each I introduce a complex problem regarding the impact of information flows on auction's performance which has not been addressed by prior auction literature. The first two essays (Chapter 1 and 2) are empirical studies in the context of online secondary market B2B auctions while the third essay (Chapter 3) is a theoretical investigation and will contribute to the B2B procurement auction literature. The findings from this dissertation have managerial implications of how/when auctioneers can improve the efficiency or success of their operations. B2B auctions are new types of ventures which have begun to shape how industries of all types trade goods. Online B2B auctions have also become particularly popular for industrial procurement and liquidation purposes. By using online B2B auctions companies can benefit by creating competition when auctioning off goods or contracts to business customers. B2B Procurement auctions− where the buyer runs an auction to procure goods and services from suppliers− have been documented as saving firms millions of dollars by lowering the cost of procurement. On the other hand, B2B auctions are also commonly used by sellers in `secondary market' to liquidate the left-over goods to business buyers in a timely fashion. In order to maximize revenues in either both industrial procurement or secondary market settings, auctioneers should understand how the auction participants behave and react to the available market information or auction design. Auctioneers can then use this knowledge to improve the performance of their B2B auctions by choosing the right auction design or strategies. In the first essay, I investigate how an online B2B secondary market auction environment can provide several sources of information that can be used by bidders to form their bids. One such information set that has been relatively understudied in the literature pertains to reference prices available to the bidder from other concurrent and comparable auctions. I will examine how reference prices from such auctions affect bidding behavior on the focal auction conditioning on bidders' types. I will use longitudinal data of auctions and bids for more than 4000 B2B auctions collected from a large liquidator firm in North America. In the second essay, I report on the results of a field experiment that I carried out on a secondary market auction site of another one of the nation's largest B2B wholesale liquidators. The design of this field experiment on iPad marketplace is directly aimed at understanding how (i) the starting price of the auction, and (ii) the number of auctions for a specific (model, quality), i.e., the supply of that product, interact to impact the auction final price. I also explore how a seller should manage the product differentiation so that she auctions off the right mix and supply of products at the reasonable starting prices. Finally, in the last essay, I study a norm used in many procurement auctions in which buyers grant the `Right of First Refusal' (ROFR) to a favored supplier. Under ROFR, the favored supplier sees the bids of all other participating suppliers and has the opportunity to match the (current) winning bid. I verify the conventional wisdom that ROFR increases the buyer's procurement cost in a single auction setting. With a looming second auction in the future (with the same participating suppliers), I show that the buyer lowers his procurement cost by granting the ROFR to a supplier. The analytical findings of this essay highlights the critical role of information flows and the timing of information-release in procurement auctions with ROFR

Interacting Like Humans? Understanding the Effect of Anthropomorphism on Consumer’s Willingness to Pay in Online Auctions

Most research examining individuals’ bidding behavior in online auctions has used the lens of a rational decision making process. However, bidding behavior is also influenced by non-rational factors. Anthropomorphism, attributing human characteristics to a non-human object, has been studied in many disciplines, but has not been investigated in online auctions. This study aims to identify whether auditory and visual design factors for a non-human product would induce anthropomorphism and impact individuals' bidding decision. Results show that visual design induces individuals’ anthropomorphism and also impacts bidding decisions

INFORMATION SHARING AND PRICE DYNAMICS IN B2B DIGITAL SYSTEMS

While multiple studies have investigated the digital ecosystems in the B2C sectors, empirical research on the upstream of the supply chain is still underexplored. This paper examines the case when a digital platform is incorporated into the century-old auction systems. This work offers insights into B2B markets and at the same time, an interesting instance where different pricing mechanisms (online posted price and auctions) co-exit. We investigate how the information of the new digital posted price channel can influence buyers’ learning behaviors and consequently, the price dynamics in the auction market. Our empirical analysis reveals that multiple information signals can play a role. While sellers’ high price and high-volume sales signals can partially dimmish the existing declining price trend in the sequential auctions where the prices from the earlier auction rounds tend to be higher than from the latter, this information effect does not persist over time. These results highlight the potential benefit of cooperating e-commerce with an auction channel for sellers and the shift in buyers’ behaviors in responding to an additional platform in a B2B market

The Happiness Premium: The Impact of Emotion on Individuals’ Willingness to Pay in Online Auctions

Much research across various disciplines has studied individuals’ bidding behavior in online auctions. Emotion is an important factor affecting individual behavior, but we know little about its effects in online auctions. We conducted a lab experiment to investigate the impact of positive emotion on individuals’ willingness to pay in online auctions. We found that individuals with positive emotions bid more than those with neutral emotions; that is, they paid a “happiness premium” of about 10 percent. The effect size was medium (Cohen’s d = 0.51). This study contributes to electronic commerce literature by identifying emotion as an important factor affecting online auction behavior. The findings also provide guidance to auction website design: websites can increase bid amounts by inducing positive emotions in potential customers

Beyond posted prices: the past, present and future of participative pricing mechanisms

Driven by the low transaction costs and interactive nature of the internet, customer participation in the price-setting process has increased. These changes were first brought about by the rise of online auctions in the early 2000s, followed by the emergence of newer participative mechanisms. Today, platforms such as eBay have popularized online auctions on a global scale, Priceline has made headlines with its name-your-own-price (NYOP) business model, and Humble Bundle has enabled independent musicians and game developers to market their works through pay-what-you-want (PWYW) pricing. Advertising exchanges conduct several hundred million individual auctions per day to sell online advertising slots. These are just a few examples of participative pricing in transactions among consumers or businesses. In parallel, academic research on participative pricing has blossomed in recent years, with an overarching concern over the profitability and other marketing implications these mechanisms have on sellers and buyers. The present paper contributes to this literature in three ways. First, we propose a definition of participative pricing mechanisms, as well as a useful taxonomy. Second, we discuss the current understanding by synthesizing conceptual and empirical academic literature. Third, we outline promising research questions with a key focus on the related behavioral aspects of buyers and sellers

EXPLORING AND MODELING OF BIDDING BEHAVIOR AND STRATEGIES OF ONLINE AUCTIONS

Internet auctions, as an exemplar of the recent boom in e-commerce, are grow- ing faster than ever in the last decade. Understanding the reasons why bidders be- have a certain way allows invaluable insight into the auction process. This research focuses on methods for modeling, testing and estimation of bidders' behavior and strategies. I start my discussion with bid shading, which is a common strategy bidders believe obtains the lowest possible price. While almost all bidders shade their bids, at least to some degree, it is impossible to infer the degree and volume of shaded bids directly from observed bidding data. In fact, most bidding data only allows researchers to observe the resulting price process, i.e. whether prices increase fast (due to little shading) or whether they slow down (when all bidders shade their bids). In this work, I propose an agent-based model that simulates bidders with different bidding strategies and their interaction with one another. The model is calibrated (and hence properties about the propensity and degree of shaded bids are estimated) by matching the emerging simulated price process with that of the observed auction data using genetic algorithms. From a statistical point of view, this is challenging because it requires matching functional draws from simulated and real price processes. I propose several competing fitness functions and explore how the choice alters the resulting ABM calibration. The method is applied to the context of eBay auctions for digital cameras and show that a balanced fitness function yields the best results. Furthermore, in light of the discrepancy find from the model in bidders' be- havior and optimal strategies proposed from online auction literature. I extract empirical bidding strategies from auction winners and utilize the agent based model to simulate and test the performance of twenty-four different empirical and theo- retical strategies. The experiment results suggest that some empirical strategies perform robustly when compared to theoretical strategies and taking into account other bidders' ability to learn. In addition, I expended the online auction framework from single auction to multiple auction simulation, which acts as a platform for investigating and test- ing more complicated situations that involves the competition among concurrent auctions. This framework facilitates my investigation of bidders' switching behavior and enables me to answer a series questions. For example, is it beneficial for auction website to promote bidders' switching behavior? Will bidders and even sellers get any advantage from bidders' switching? What is the best auction recommendation strategy for online auction website to obtain higher profit and/or a better customer experience? Through careful experiment design, it has been showed that higher switching frequency leads to higher profit for auction website and reduces the price dispersion, which leads to reduced risk for both bidders and sellers. In addition, the best auction recommendation strategy is providing the five earliest closing auctions so that bidders can choose the lowest price auction

An Economic Analysis of Consumer Learning on Entertainment Shopping Websites

Online entertainment shopping, normally supported by the pay-to-bid auction mechanism, represents an innovative business model in e-commerce. Because the unique selling mechanism combines features of shopping and online auction, consumers expect both monetary return and entertainment value from their participation. We propose a dynamic structural model to analyze consumer behaviors on entertainment shopping websites. The model captures the consumer learning process, based both on individual participation experiences and also on observational learning of historical auction information. We estimate the model using a large data set from an online entertainment shopping website. Results show that consumers’ initial participation incentives mainly come from a significant overestimation of the entertainment value and an obvious underestimation of the auction competition. Both types of learning contribute to a general decreasing participation trend among consumers over time. Our model provides both a theoretical explanation and empirical evidence of the consumer churn issue. It further identifies two groups of consumers with different risk characteristics: One group is risk-averse and quits using the website before effective learning takes place, while the other group exhibits risk-seeking behavior and overly commits to the auction games. Based on the estimated parameters of the model, we perform counterfactual analyses to evaluate the effects of policy changes on consumers’ participation behaviors. We discuss several important design implications and recommend strategies for building a sustainable business model in the entertainment shopping industry

Hybrid Advertising Auctions

Several major websites offer hybrid auctions that allow advertisers to bid on a per-impression or a per-click basis. We present the first analysis of this hybrid advertising auction setting. The conventional wisdom is that brand advertisers (e.g. Coca-Cola) will bid per impression, while direct response advertisers (e.g. Amazon.com) will bid per click. We analyze a theoretical model of advertiser bidding to ask whether this conventional wisdom will hold up in practice. We find the opposite in a static game: brand advertisers bid per click, while direct response advertisers bid per impression. In a more realistic repeated game, we find that direct response advertisers bid per click, but brand advertisers may profitably alternate between bidding for clicks and bidding for impressions. The analysis implies that sellers of online advertising (a) may sometimes prefer not to offer advertisers multiple bidding options, (b) should try to ascertain advertisers' types when they do use hybrid auctions, and (c) should consider advertisers' strategic incentives when forming click-through rate expectations in hybrid auction formats