Prior research with consumable goods has consistently found that consumers have a preference for greater variety when selecting items simultaneously as a bundle, rather than as a sequential series of individual decisions. However, digital information goods have a number of important differences from consumable goods that may impact variety-seeking behavior. In three experiments, we address two general research questions. First, as a precursor to studying digital goods, we disentangle the role of bundle cohesion (i.e., item relatedness) from the role of timing (simultaneous vs. sequential choice) as factors in variety seeking with consumable goods. Next, based on differences between digital and consumable goods, we theorize differences in the behavioral effects of bundle cohesion and timing on variety preferences for digital goods. The results show a reduction of influences upon variety-seeking behavior with digital goods, providing important implications for the sellers of such goods in contrast to what has been suggested for consumable goods. Therefore, a key takeaway is that, for digital goods such as music, the use of consumer-driven bundling variations does not suggest an advantage in terms of their ability to affect consumers' variety-seeking behavior. > >
Advancements in information technology offer opportunities for designing and deploying innovative market mechanisms that can improve the allocation and procurement processes of businesses. For example, combinatorial auctions-in which bidders can bid on combinations of goods-have been shown to increase the economic efficiency of a trade when goods have complementarities. However, the lack of real-time decision support tools for bidders has prevented this mechanism from reaching its full potential. With the objective of facilitating bidder participation in combinatorial auctions, this study, using recent research in real-time bidder support metrics, discusses several novel feedback schemes that can aid bidders in formulating combinatorial bids in real-time. The feedback schemes allow us to conduct continuous combinatorial auctions, where bidder scan submit bids at any time. Using laboratory experiments with two different setups, we compare the economic performance of the continuous mechanism under three progressively advanced levels of feedback. Our findings indicate that information feedback plays a major role in influencing the economic outcomes of combinatorial auctions. We compare several important bid characteristics to explain the observed differences in aggregate measures. This study advances the ongoing research on combinatorial auctions by developing continuous auctions that differentiate themselves from earlier combinatorial auction mechanisms by facilitating free flowing participation of bidders and providing exact prices of bundles on demand in real time. For practitioners, the study provides insights on how the nature of feedback can influence the economic outcomes of a complex trading mechanism
Prior IS research on technological change has focused primarily on organizational information systems and technology innovation; however, there is a growing need to understand the dynamics of supply-side forces in the introduction of new technologies. In this paper we investigate how the interdependencies among information technology components, products, and infrastructure affect the release of new technologies. Going beyond the ad hoc heuristic approaches applied in previous studies, we empirically validate the existence of several patterns of supply-side technology relationships in the context of wireless networking. We use vector autoregression (VAR) to model the comovements of new component, product, and infrastructure introductions and provide evidence of strong Granger-causal interdependencies. We also demonstrate that substantial improvements in forecasting can be gained by incorporating these cross-level effects into models of technological change. This paper provides some of the first research that empirically demonstrates these cross-level effects and also provides an exposition of VAR methodology for both analysis and forecasting in IS research.
Electronic auctions are increasingly being used to facilitate the procurement of goods and services in organizations. Multiattribute auctions, which allow bids on multiple dimensions of the product and not just price, are information technology-enabled sourcing mechanisms that can increase the efficiency of procurement for configurable goods and services compared to price-only auctions. Given the strategic nature of procurement auctions, the amount of information concerning the buyer's preferences that is disclosed to the suppliers has implications on the profits of the buyer and the suppliers and, consequently, on the long-term relationship between them. This study explores novel feedback schemes for multisourcing multiattribute auctions that require limited exchange of strategic information between the buyer and the suppliers. To study the impact of feedback on the outcomes and dynamics of the auctions, we conduct laboratory experiments wherein we analyze bidder behavior and economic outcomes under three different treatment conditions with different types of information feedback. Our results indicate that, in contrast to winner-take-all multiattribute auctions, multisourcing multiattribute auctions, with potentially multiple winners, allow bidders (i.e., suppliers) to extract more profit when greater transparency in terms of provisional allocations and prices is provided. We develop several insights for mechanism designers toward developing sustainable procurement auctions that efficiently allocate multiple units of an asset with multiple negotiable attributes among multiple suppliers.
Initially popularized by Amazon.com, recommendation technologies have become widespread over the past several years. However, the types of recommendations available to the users in these recommender systems are typically determined by the vendor and therefore are not flexible. In this paper, we address this problem by presenting the recommendation query language REQUEST that allows users to customize recommendations by formulating them in the ways satisfying personalized needs of the users. REQUEST is based on the multidimensional model of recommender systems that supports additional contextual dimensions besides traditional User and Item dimensions and also OLAP-type aggregation and filtering capabilities. This paper also presents the recommendation algebra RA, shows how REQUEST recommendations can be mapped into this algebra, and analyzes the expressive power of the query language and the algebra. This paper also shows how users can customize their recommendations using REQUEST queries through a series of examples.
This paper presents analytical, computational, and empirical analyses of strategies for intelligent bid formulations in online auctions. We present results related to a weighted-average ascending price auction mechanism that is designed to provide opaque feedback information to bidders and presents a challenge in formulating appropriate bids. Using limited information provided by the mechanism, we design strategies for software agents to make bids intelligently. In particular, we derive analytical results for the important characteristics of the auction, which allow estimation of the key parameters; we then use these theoretical results to design several bidding strategies. We demonstrate the validity of designed strategies using a discrete event simulation model that resembles the mechanisms used in treasury bills auctions, business-to-consumer (B2C) auctions, and auctions for environmental emission allowances. In addition, using the data generated by the simulation model, we show that intelligent strategies can provide a high probability of winning an auction without significant loss in surplus.
A major problem for firms making information technology investment decisions is predicting and understanding the effects of future technological developments on the value of present technologies. Failure to adequately address this problem can result in wasted organization resources in acquiring, developing, managing, and training employees in the use of technologies that are short-lived and fail to produce adequate return on investment. The sheer number of available technologies and the complex set of relationships among them make IT landscape analysis extremely challenging. Most IT-consuming firms rely on third parties and suppliers for strategic recommendations on IT investments, which can lead to biased and generic advice. We address this problem by defining a new set of constructs and methodologies upon which we develop an IT ecosystem model. The objective of these artifacts is to provide a formal problem representation structure for the analysis of information technology development trends and to reduce the complexity of the IT landscape for practitioners making IT investment decisions. We adopt a process theory perspective and use a combination of visual mapping and quantification strategies to develop our artifacts and a state diagram-based technique to represent evolutionary transitions over time. We illustrate our approach using two exemplars: digital music technologies and wireless networking technologies. We evaluate the utility of our approach by conducting in-depth interviews with IT industry experts and demonstrate the contribution of our approach relative to existing techniques for technology forecasting.
Many auctions involve selling several distinct items simultaneously, where bidders can bid on the whole or any part of the lot. Such auctions are referred to as combinatorial auctions. Examples of such auctions include truck delivery routes,industrial procurement, and FCC spectrum. Determining winners in such auctions is an NP-hard problem, and significant research is being conducted in this area. However, multiple- round (iterative) combinatorial auctions present significant challenges in bid formulations as well. Because the combinatorial dynamics in iterative auctions can make a given bid part of a winning and nonwinning set of bids without any changes in the bid, bidders are usually not able to evaluate whether they should revise their bid at a given point in time or not. Therefore, in this paper we address various computational problems that are relevant from the bidder's perspective. In particular, we introduce two bid evaluation metrics that can be used by bidders to determine whether any given bid can be a part of the winning allocation and explore their theoretical properties. Based on these metrics, we also develop efficient data structures and algorithms that provide comprehensive information about the current state of the auction at any time, which can help bidders in evaluating their bids and bidding strategies. Our approach uses exponential memory storage but provides fast incremental update for new bids, thereby facilitating bidder support for real-time iterative combinatorial auctions.