Impact of Information Technology on Agricultural Commodity Auctions in India

Empirical research on the value of information technology investments in the information systems literature has primarily focused on the use of IT by businesses and multinational firms. The impact of IT on the global agricultural supply chain has largely been ignored in the IS literature. Auctions to buy and sell large volumes of agricultural commodities are widely prevalent in diverse regions of the world and are an important part of the agricultural supply chain. In an effort to increase efficiency, commodity auctions have been experimenting with online formats in recent years. Such online auctions have generated significant interest in the trade press because of their potential to generate higher commodity prices for producers, reduce unfair trading practices by middlemen, and bridge the digital divide. We analyze transaction data from a recently set up online auction in India that trades in various grades of coffee. We model the impact of lower transaction costs, daily operations, less collusive behavior among buyers, and learning curve effects on the selling price of coffee in the online auction. We estimate the parameters of the model by comparing the prices in the electronic auction with those of the same grade of coffee at physical auctions held weekly. We find that electronic auction prices are 4 percent higher and the difference is statistically significant. Further, we find that the price differential is higher for coffee grades that have higher price volatility and that are traded less frequently in the physical exchange. We also find that the price differential increases over time as buyers become more familiar with the benefits of the electronic trading format

Pricing in Online Auction Procurement: A Review of Empirical Methods and Current Understandings

The online auction has become an important channel for procurement and sourcing management. As firms often expect lower procurement prices through online auctions, how the prices are determined in online auctions should be of major interest to procurement managers and supply chain researchers. Despite the abundant empirical studies on online auction prices, an aggregated view is still absent. This study fills this gap with a review of extant studies. More specifically, this study provides summaries of all major theories behind online auction pricing, defines and analyzes often encountered econometric issues, and discusses how the treatments of these issues have been operationalized. Towards the end, existing findings on determinants of online auction prices are integrated and examined. The purpose of this study is to provide a convenient and precise package of current studies for researchers and professional

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

The BARISTA: A model for bid arrivals in online auctions

The arrival process of bidders and bids in online auctions is important for studying and modeling supply and demand in the online marketplace. A popular assumption in the online auction literature is that a Poisson bidder arrival process is a reasonable approximation. This approximation underlies theoretical derivations, statistical models and simulations used in field studies. However, when it comes to the bid arrivals, empirical research has shown that the process is far from Poisson, with early bidding and last-moment bids taking place. An additional feature that has been reported by various authors is an apparent self-similarity in the bid arrival process. Despite the wide evidence for the changing bidding intensities and the self-similarity, there has been no rigorous attempt at developing a model that adequately approximates bid arrivals and accounts for these features. The goal of this paper is to introduce a family of distributions that well-approximate the bid time distribution in hard-close auctions. We call this the BARISTA process (Bid ARrivals In STAges) because of its ability to generate different intensities at different stages. We describe the properties of this model, show how to simulate bid arrivals from it, and how to use it for estimation and inference. We illustrate its power and usefulness by fitting simulated and real data from eBay.com. Finally, we show how a Poisson bidder arrival process relates to a BARISTA bid arrival process.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS117 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Role of Dealers in Electronic Markets: Empirical Insights from Online Auctions

This study examines the impact of intermediaries (dealers) in online Consumer-to-Consumer (C2C) market. Online C2C transactions, such as the Internet auctions on eBay, are one of the most successful forms of electronic commerce (e-commerce). It has been suggested by many scholars that the Internet or electronic markets will eliminate intermediaries by lowering search cost and allowing direct and efficient interactions between sellers and buyers. However, a close examination of the market mechanism indicates that many functions provided by intermediaries are indispensable. Specifically, we consider intermediaries’ role in price discovery and trust building in electronic markets. Intermediaries provide a buffer for temporary misalignment between supply and demand by buying low and selling high, which provides product liquidity to buyers and sellers in online markets. Intermediaries also help build trust by engaging in transactions with risk-averse buyers and sellers who otherwise will not participate in the market. Using a dataset from eBay’s online auctions, we examine empirically these two functions in online C2C auction markets. We find that the presence of dealers has a significant impact on market liquidity, resulting in more successful trades and higher auction prices. In addition, we find that dealers are more likely to engage in transactions with less established sellers. Their presence reduces the reputation penalty faced by these players and further facilitates the success of auctions

Online Combinatorial Auctions for Resource Allocation with Supply Costs and Capacity Limits

We study a general online combinatorial auction problem in algorithmic mechanism design. A provider allocates multiple types of capacity-limited resources to customers that arrive in a sequential and arbitrary manner. Each customer has a private valuation function on bundles of resources that she can purchase (e.g., a combination of different resources such as CPU and RAM in cloud computing). The provider charges payment from customers who purchase a bundle of resources and incurs an increasing supply cost with respect to the totality of resources allocated. The goal is to maximize the social welfare, namely, the total valuation of customers for their purchased bundles, minus the total supply cost of the provider for all the resources that have been allocated. We adopt the competitive analysis framework and provide posted-price mechanisms with optimal competitive ratios. Our pricing mechanism is optimal in the sense that no other online algorithms can achieve a better competitive ratio. We validate the theoretic results via empirical studies of online resource allocation in cloud computing. Our numerical results demonstrate that the proposed pricing mechanism is competitive and robust against system uncertainties and outperforms existing benchmarks.Comment: arXiv admin note: text overlap with arXiv:2004.0964