Gupta, Alok

Special Section IntroductionãUbiquitous IT and Digital Vulnerabilities (Information Systems Research, 2016)

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Authors:

Ransbotham, Sam; Fichman, Robert G.; Gopal, Ram D.; Gupta, Alok;

Abstract:

While information technology benefits society in numerous ways, it unfortunately also has potential to create new vulnerabilities. This special issue intends to stimulate thought and research into understanding and mitigating these vulnerabilities. We identify four mechanisms by which ubiquitous computing makes various entities (people, devices, organizations, societies, etc.) more vulnerable, including: increased visibility, enhanced cloaking, increased interconnectedness, and decreased costs. We use the papers in the special issue to explain these mechanisms, and then outline a research agenda for future work on digital vulnerabilities spanning four areas that are, or could become, significant societal problems with implications at multiple levels of analysis: Online harassment and incivility, technology-driven economic inequality, industrial Internet of Things, and algorithmic ethics and bias.

A Multiagent Competitive Gaming Platform to Address Societal Challenges (MIS Quarterly, 2016)

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Authors:

Ketter, Wolfgang; Peters, Markus; Collins, John; Gupta, Alok;

Abstract:

The shift toward sustainable electricity systems is one of the grand challenges of the 21st century. Decentralized production from renewable sources, electric mobility, and related advances are at odds with traditional power systems where central, large-scale generation of electricity follows inelastic consumer demand. Information systems innovations can enable new forms of dynamic electricity trading that leverage real-time consumption information and that use price signals to incentivize sustainable consumption behaviors. However, the best designs for these innovations, and the societal implications of different design choices, are largely unclear. We are addressing these challenges through the Power Trading Agent Competition (Power TAC), a competitive gaming platform on which numerous research groups now jointly devise, benchmark, and improve IS-based solutions to the sustainable electricity challenge. Based on the Power TAC community's results, we give preliminary empirical evidence for the efficacy of competitive gaming platforms, and for the community's contributions toward resolving the sustainable electricity challenge.

Exploring Bidder Heterogeneity in Multichannel Sequential B2B Auctions (MIS Quarterly, 2016)

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Authors:

Lu, Yixin; Gupta, Alok; Ketter, Wolfgang; Heck, Eric van;

Abstract:

The proliferation of online auctions has attracted significant research interest in understanding real-life bidding behavior. However, most of the empirical work has focused on business-to-consumer (B2C) auctions. A natural question is whether the findings obtained from B2C auctions are applicable to business-to-business (B2B) auctions, which often involve much higher stakes. In this paper, we examine how professional bidders choose their bidding strategies in multichannel, sequential B2B auctions. Using an extensive data set from the world's largest B2B market for cut flowers, we find a stable taxonomy of bidding behavior and identify five distinctive bidding strategies. In addition, we demonstrate that bidders' choice of strategies is associated with their demand, budget constraint, and transaction cost. These findings challenge the conventional view that bidders' bidding strategies will converge as they gain experience. We also analyze the economic impacts of different strategies. Our results provide useful implications for practical design of B2B auctions.

Joint Product Improvement by Client and Customer Support Center: The Role of Gain-Share Contracts in Coordination (Information Systems Research, 2014)

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Authors:

Bhattacharya, Shantanu; Gupta, Alok; Hasija, Sameer;

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We study the role of different contract types in coordinating the joint product improvement effort of a client and a customer support center. The customer support center's costly efforts at joint product improvement include transcribing and analyzing customer feedback, analyzing market trends, and investing in product design. Yet this cooperative role must be adequately incentivized by the client, since it could lead to fewer service requests and hence lower revenues for the customer support center. We model this problem as a sequential game with double-sided moral hazard in a principal-agent framework (in which the client is the principal). We follow the contracting literature in modeling the effort of the customer support center, which is the first mover, as either unobservable or observable; in either case, the efforts are unverifiable and so cannot be contracted on directly. We show that it is optimal for the client to offer the customer support center a linear gain-share contract when efforts are unobservable, even though it can yield only the second-best solution for the client. We also show that the cost-plus contracts widely used in practice do not obtain the optimal solution. However, we demonstrate that if efforts are observable then a gain-share and cost-plus options- based contract is optimal and will also yield the first-best solution. Our research provides a systematic theoretical framework that accounts for the prevalence of gain-share contracts in the IT industry's joint improvement efforts, and it provides guiding principles for understanding the increased role for customer support centers in product improvement.

IMPACT OF INFORMATION FEEDBACK IN CONTINUOUS COMBINATORIAL AUCTIONS: AN EXPERIMENTAL STUDY OF ECONOMIC PERFORMANCE. (MIS Quarterly, 2013)

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Authors:

Adomavicius, Gediminas; Curley, Shawn P.; Gupta, Alok; Sanyal, Pallab;

Abstract:

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

TRANSPARENCY STRATEGY: COMPETING WITH INFORMATION IN A DIGITAL WORLD. (MIS Quarterly, 2013)

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Authors:

Granados, Nelson; Gupta, Alok;

Abstract:

We contend that in order to compete effectively in a digital business environment, firms should develop a transparency strategy by selectively disclosing information outside the boundaries of the firm. We make the case for transparency strategy by showing why it is relevant in the digital business world, and the consequences of not having such a strategy. We then provide some foundations to develop the strategy and make a call for research.

Online and Offline Demand and Price Elasticities: Evidence from the Air Travel Industry. (Information Systems Research, 2012)

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Authors:

Granados, Nelson; Gupta, Alok; Kauffman, Robert J.;

Abstract:

The Internet has brought consumers increased access to information to make purchase decisions. One of the expected consequences is an increase in the price elasticity of demand, or the percent change in demand caused by a percent change in price, because consumers are better able to compare offerings from multiple suppliers. In this paper, we analyze the impact of the Internet on demand, by comparing the demand functions in the Internet and traditional air travel channels. We use a data set that contains information for millions of records of airline ticket sales in both online and offline channels. The results suggest that consumer demand in the Internet channel is more price elastic for both transparent and opaque online travel agencies (OTAs), in part, because of more leisure travelers self-selecting the online channel, relative to business travelers. Yet, after controlling for this channel self-selection effect, we still find differences in price elasticity across channels. We find that the opaque OTAs are more price elastic than the transparent OTAs, which suggests that product information can mitigate the price pressures that arise from Internet-enabled price comparisons. We discuss the broader implications for multichannel pricing strategy and for the transparency-based design of online selling mechanisms.

Modeling Supply-Side Dynamics of IT Components, Products, and Infrastructure: An Empirical Analysis Using Vector Autoregression. (Information Systems Research, 2012)

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Authors:

Adomavicius, Gediminas; Bockstedt, Jesse C.; Gupta, Alok;

Abstract:

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.

Putting Money Where the Mouths Are: The Relation Between Venture Financing and Electronic Word-of-Mouth. (Information Systems Research, 2012)

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Authors:

Aggarwal, Rohit; Gopal, Ram D.; Gupta, Alok; Singh, Harpreet;

Abstract:

External financing is critical to ventures that do not have a revenue source but need to recruit employees, develop products, pay suppliers, and market their products/services. There is an increasing belief among entrepreneurs that electronic word-of-mouth (eWOM), specifically blog coverage, can aid in achieving venture capital financing. Conflicting findings reported by past studies examining eWOM make it unclear what to make of such beliefs of entrepreneurs. Even if there were generally agreed-upon results, a stream of literature indicates that because of the differences in traits between the prior investigated contexts and venture capital financing, the findings from the prior studies cannot be generalized to venture capital financing. Extant studies also fall short in examining the role of time and the status of entities generating eWOM in determining the influence of eWOM on decision making. To address this dearth of literature in a context that attracts billions of dollars every year, we investigate the effect of eWOM on venture capital financing. This study entails the challenging task of gathering data from hundreds of ventures along with other sources including VentureXpert, surveys, Google Blogsearch, Lexis-Nexis, and Archive.org. The key findings of our econometric analysis are that the impact of negative eWOM is greater than is the impact of positive eWOM and that the effect of eWOM on financing decreases with the progress through the financing stages. We also find that the eWOM of popular bloggers helps ventures in getting higher funding amounts and valuations. The empirical model used in this work accounts for inherent selection biases of entrepreneurs and venture capitalists, and we conduct numerous robustness checks for potential issues of endogeneity, selection bias, nonlinearities, and popularity cutoff for blogs. The findings have important implications for entrepreneurs and suggest ways by which entrepreneurs can take advantage of eWOM.

Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes. (Information Systems Research, 2012)

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Authors:

Ketter, Wolfgang; Collins, John; Gini, Maria; Gupta, Alok; Schrater, Paul;

Abstract:

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real time. We describe a family of statistical models that addresses these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These "regime" models are developed using statistical analysis of historical data and are used in real time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management, a supply chain environment characterized by competitive procurement, sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and long-term resource allocation decisions. Results show that our method outperforms more traditional short- and long-term predictive modeling approaches.

Effect of Information Feedback on the Outcomes and Dynamics of Multisourcing Multiattribute Procurement Auctions. (Journal of Management Information Systems, 2012)

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Authors:

Adomavicius, Gediminas; Gupta, Alok; Sanyal, Pallab;

Abstract:

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.

ON THE USE OF NEUROPHYSIOLOGICAL TOOLS IN IS RESEARCH: DEVELOPING A RESEARCH AGENDA FOR NEUROIS. (MIS Quarterly, 2012)

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Authors:

Dimoka, Angelika; Banker, Rajiv D.; Benbasat, Izak; Davis, Fred D.; Dennis, Alan R.; Gefen, David;

Abstract:

This article discusses the role of commonly used neurophysiological tools such as psychophysiological tools (e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems research. There is heated interest now in the social sciences in capturing presumably objective data directly from the human body, and this interest in neurophysiological tools has also been gaining momentum in IS research (termed NeuroIS). This article first reviews commonly used neurophysiological tools with regard to their major strengths and weaknesses. It then discusses several promising application areas and research questions where IS researchers can benefit from the use of neurophysiological data. The proposed research topics are presented within three thematic areas: (1) development and use of systems, (2) IS strategy and business outcomes, and (3) group work and decision support. The article concludes with recommendations on how to use neurophysiological tools in IS research along with a set of practical suggestions for developing a research agenda for NeuroIS and establishing NeuroIS as a viable subfield in the IS literature.

GROWTH AND SUSTAINABILITY OF MANAGED SECURITY SERVICES NETWORKS: AN ECONOMIC PERSPECTIVE. (MIS Quarterly, 2012)

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Authors:

Gupta, Alok; Zhdanov, Dmitry;

Abstract:

Managed security service provider (MSSP) networks are a form of collaboration where several firms share resources such as diagnostics, prevention tools, and policies to provide security for their computer networks. While the decision to outsource the security operations of an organization may seem counterintuitive, there are potential benefits from joining an MSSP network that include pooling of risk and access to more security-enabling resources and expertise. We examine structural results explaining the reasons firms join an MSSP network, and characterize the growth of MSSP network size under different forms of ownership (monopoly versus consortium). We find that the need for an initial investment in MSSP networks (which is necessary to overcome the stalling effect) only affects the optimal network size for a consortium but has no impact on the optimal network size for a profit-maximizing monopolist. Our results provide an explanation why the majority of the MSSPs are for-profit entities and consortium-based MSSPs are less common. Such a market structure can be attributed to the potential for larger size by the for-profit MSSP owner combined with beneficial pricing structure and a lack of growth uncertainty for the early clients.

An Analysis of Incentives for Network Infrastructure Investment Under Different Pricing Strategies. (Information Systems Research, 2011)

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Authors:

Gupta, Alok; Jukic, Boris; Stahl, Dale O.; Whinston, Andrew B.;

Abstract:

The Internet is making a significant transition from primarily a network of desktop computers to a network variety of connected information devices such as personal digital assistants and global positioning system-based devices. On the other hand, new paradigms such as overlay networks are defining service-based logical architecture for the network services that make locating content and routing more efficient. Along with Internet2' s proposed service-based routing, overlay networks will create a new set of challenges in the provision and management of content over the network. However, a lack of proper infrastructure investment incentive may lead to an environment where network growth may not keep pace with the service requirements. In this paper, we present an analysis of investment incentives for network infrastructure owners under two different pricing strategies: congestion-based negative externality pricing and the prevalent flat-rate pricing. We develop a theoretically motivated gradient-based heuristic to compute maximum capacity that a network provider will be willing to invest in under different pricing schemes. The heuristic appropriates different capacities to different network components based on demand for these components. We then use a simulation model to compare the impact of dynamic congestion-based pricing with flat-rate pricing on the choice of capacity level by the infrastructure provider. The simulation model implements the heuristic and ensures that near-optimal level of capacity is allocated to each network component by checking theoretical optimality conditions. We investigate the impact of a variety of factors, including the per unit cost of capacity of a network resource, average value of the users' requests, average level of users' tolerance for delay, and the level of exogenous demand for services on the network. Our results indicate that relationships between these factors are crucial in determining which of the two pricing schemes results in a higher level of socially optimal network capacity. The simulation results provide a possible explanation for the evolution of the Internet pricing from time-based to flat-rate pricing. The results also indicate that regardless of how these factors are related, the average stream of the net benefits realized under congestion-based pricing tends to be higher than the average net benefits realized under flat-rate pricing. These central results point to the fallacy of the arguments presented by the supporters of net neutrality that do not consider the incentives for private investment in network capacity.

Information Transparency in Business-to-Consumer Markets: Concepts, Framework, and Research Agenda. (Information Systems Research, 2010)

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Authors:

Granados, Nelson; Gupta, Alok; Kauffman, Robert J.;

Abstract:

The Internet has brought about significant changes in the availability of market information in many industries. E-commerce technologies provide sellers with opportunities to design electronic mercantile mechanisms that reveal, conceal, bias, and distort market information, depending on their goals and market position (e.g., suppliers versus intermediaries). In particular, in information-intensive industries where electronic markets play an important role, many firms are using advanced technologies to put innovative strategies into play that are based on the provision of differential information to their customers. We examine the role of information transparency in electronic markets. We contend that there is an opportunity to develop research on sellers' strategies regarding information disclosure to customers and competitors. For that purpose, we develop a set of concepts and a framework to guide future research. We then propose an interdisciplinary agenda for research on the emerging and increasingly important topic of transparency strategy, which we define as the set of policies and decisions that a firm makes to disclose, conceal, bias, or distort market information.

Designing Smart Markets. (Information Systems Research, 2010)

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Authors:

Bichler, Martin; Gupta, Alok; Ketter, Wolfgang;

Abstract:

Electronic markets have been a core topic of information systems (IS) research for last three decades. We focus on a more recent phenomenon: smart markets. This phenomenon is starting to draw considerable interdisciplinary attention from the researchers in computer science, operations research, and economics communities. The objective of this commentary is to identify and outline fruitful research areas where IS researchers can provide valuable contributions. The idea of smart markets revolves around using theoretically supported computational tools to both understand the characteristics of complex trading environments and multiechelon markets and help human decision makers make real-time decisions in these complex environments. We outline the research opportunities for complex trading environments primarily from the perspective of design of computational tools to analyze individual market organization and provide decision support in these complex environments. In addition, we present broad research opportunities that computational platforms can provide, including implications for policy and regulatory research.

Designing Intelligent Software Agents for Auctions with Limited Information Feedback. (Information Systems Research, 2009)

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Authors:

Adomavicius, Gediminas; Gupta, Alok; Zhdanov, Dmitry;

Abstract:

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.

OVERLAPPING ONLINE AUCTIONS: EMPIRICAL CHARACTERIZATION OF BIDDER STRATEGIES AND AUCTION PRICES. (MIS Quarterly, 2009)

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Authors:

Bapna, Ravi; Chang, Seokjoo Andrew; Goes, Paulo B.; Gupta, Alok;

Abstract:

Online auctions enable market-level interactions or interdependency of outcomes, which were not observed in physical auctions. One such set of interactions takes place when multiple auctions are conducted to sell identical items by an identical seller in an overlapping manner. This research focuses on overlapping auctions, their interactions, and the related impact on bidder behavior. We introduce the notion of auction "overlap" and examine the impact of market-level factors such as the price information revealed from prior auctions, degree of overlap, the auction format, and the overall market supply on a given auction's price. Despite a competitive setting, we find that, ceteris paribus, English auctions, on average, extract roughly 8.6 percent more revenue per unit than multiunit uniform-price Dutch auctions. We discover that the overlapping auctions attract institutional bidders, who bid in a participatory manner across multiple auctions, and that such bidders exert a downward pressure on auction prices. We find that overlap of an auction with other competing auctions has a significant negative influence on prices, and information about following auctions has a stronger negative influence than information about prior closing auctions. By estimating the expected price difference, we provide practitioners, who have private knowledge of their internal holding costs, a benchmark that can be used in deciding between using overlapping single-unit English auctions and multiunit Dutch auctions.

MAKING SENSE OF TECHNOLOGY TRENDS IN THE INFORMATION TECHNOLOGY LANDSCAPE: A DESIGN SCIENCE APPROACH. (MIS Quarterly, 2008)

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Authors:

Adomavicius, Gediminas; Bockstedt, Jesse C.; Gupta, Alok; Kauffman, Robert J.;

Abstract:

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.

Toward Comprehensive Real-Time Bidder Support in Iterative Combinatorial Auctions. (Information Systems Research, 2005)

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Authors:

Adomavicius, Gediminas; Gupta, Alok;

Abstract:

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.

USER HETEROGENEITY AND ITS IMPACT ON ELECTRONIC AUCTION MARKET DESIGN: AN EMPIRICAL EXPLORATION. (MIS Quarterly, 2004)

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Authors:

Bapna, Ravi; Goes, Paulo B.; Gupta, Alok; Jin, Yiwei;

Abstract:

While traditional information systems research emphasizes understanding of end users from perspectives such as cognitive fit and technology acceptance, it fails to consider the economic dimensions of their interactions with a system. When viewed as economic agents who participate in electronic markets, it is easy to see that users™ preferences, behaviors, personalities, and ultimately their economic welfare are intricately linked to the design of information systems. We use a data-driven, inductive approach to develop a taxonomy of bidding behavior in online auctions. Our analysis indicates significant heterogeneity exists in the user base of these representative electronic markets. Using online auction data from 1999 and 2000, we find a stable taxonomy of bidder behavior containing five types of bidding strategies. Bidders pursue different bidding strategies that, in aggregate, realize different winning likelihoods and consumer surplus. We find that technological evolution has an impact on bidders™ strategies. We demonstrate how the taxonomy of bidder behavior can be used to enhance the design of some types of information systems. These enhancements include developing usercentric bidding agents, inferring bidders™ underlying valuations to facilitate real-time auction calibration, and creating low-risk computational platforms for decision making.

GIST: A MODEL FOR DESIGN AND MANAGEMENT OF CONTENT AND INTERACTIVITY OF CUSTOMER-CENTRIC WEB SITES. (MIS Quarterly, 2004)

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Authors:

Albert, Terri C.; Goes, Paulo B.; Gupta, Alok;

Abstract:

Customer-centric Web-based systems, such as e-commerce Web sites, or sites that support customer relationship management (CRM) activities, are themselves information systems, but their design and maintenance need to follow vastly different approaches from the traditional systems lifecycle approach. Based on marketing frame- works that are applicable to the online world, and following design science principles, we develop a model to guide the design and the continuous management of such sites. The model makes extensive use of current technologies for tracking the customers and their behaviors, and combines elements of data mining and statistical analyses. A case study based on a financial services Web site is used to provide a preliminary validation and design evaluation of our approach. The case study showed considerable measured improvement in the effectiveness of the company's Web site. In addition, it also highlighted an important benefit of the our approach: the identification of previously unknown or unexpected segments of visitors. This finding can lead to promising new business opportunities.

Replicating Online Yankee Auctions to Analyze Auctioneers' and Bidders' Strategies. (Information Systems Research, 2003)

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Authors:

Bapna, Ravi; Goes, Paulo B.; Gupta, Alok;

Abstract:

We present a simulation approach that provides a relatively risk-free and cost-effective environment to examine the decision space for both bid takers and bid makers in web-based dynamic price setting processes. The applicability of the simulation platform is demonstrated for Yankee auctions in particular. We focus on the optimization of bid takers' revenue, as well as on examining the welfare implications of a range of consumer-bidding strategies--some observed, some hypothetical. While these progressive open discriminatory multi-unit auctions with discrete bid increments are made feasible by Internet technologies, little is known about their structural characteristics, or their allocative efficiency. The multi-unit and discrete nature of these mechanisms renders the traditional analytic framework of game theory intractable (Nautz and Wolfstetter 1997). The simulation is based on theoretical revenue generating properties of these auctions. We use empirical data from real online auctions to instantiate the simulation's parameters. For example, the bidding strategies of the bidders are specified based on three broad bidding strategies observed in real online auctions. The validity of the simulation model is established and subsequently the simulation model is configured to change the values of key control factors, such as the bid increment. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of bid increment, resulting in substantial losses in a market with already tight margins. The simulation tool provides a test bed for jointly exploring the combinatorial space of design choices made by the auctioneer's and the bidding strategies adopted by the bidders. For instance, a multinomial logit model reveals that endogenous factors, such as the bid increment and the absolute magnitude of the...

Integrating User Preferences and Real-Time Workload in Information Services. (Information Systems Research, 2000)

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Authors:

Konana, Prabhudev; Gupta, Alok; Whinston, Andrew B.;

Abstract:

We propose priority pricing as an on-line adaptive resource scheduling mechanism to manage real-time databases within organizations. These databases provide timely information for delay sensitive users. The proposed approach allows diverse users to optimize their own objectives while collectively maximizing organizational benefits. We rely on economic principles to derive priority prices by modeling the fixed-capacity real-time database environment as an economic system. Each priority is associated with a price and a delay, and the price is the premium (congestion toll resulting from negative externalities) for accessing the database. At optimality, the prices are equal to the aggregate delay cost imposed on all other users of the database. These priority prices are used to control admission and to schedule user jobs in the database system. The database monitors the arrival processes and the state of the system, and incrementally adjusts the prices to regulate the flow. Because our model ignores the operational intricacies of the real-time databases (e.g., intermediate queues at the CPU and disks, memory size, etc.) to maintain analytical tractability, we evaluate the performance of our pricing approach through simulation. We evaluate the database performance using both the traditional real-time database performance metrics (e.g., the number of jobs serviced on time, average tardiness) and the economic benefits (e.g., benefits to the organization). The simulation results, under various database workload parameters, show that our priority pricing mechanism not only maximizes organizational benefits but also outperforms in all aspects of traditional performance measures compared to frequently used database scheduling techniques, such as first-come-first-served, earliest deadline first and least slack first.

Extracting Consumers' Private Information for Implementing Incentive-Compatible Internet Traffic Pricing. (Journal of Management Information Systems, 2000)

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Authors:

Gupta, Alok; Jukic, Boris; Stahl, Dale O.; B.Whinston, Andrew;

Abstract:

Internet traffic pricing is necessary for the vitality of electronic commerce because uncontrolled congestion creates a detrimental effect on quality of the Internet services. Pricing approaches based on negative externality have potential to address the issue of congestion. However, most externality-based pricing approaches require the knowledge of consumers' private demand characteristics, and this requirement is often pointed out as the single most important shortcoming of these mechanisms. The fact that the Internet is a "public good" presents challenging information extraction problems for network managers in implementing any pricing mechanism. Ideally, we seek an incentive-compatible mechanism--a means of extracting the required information that provides no incentives for users to alter their behavior in an attempt to manipulate the information extraction and price setting processes. The authors present a solution based on a new nonparametric statistical technique that was developed for this purpose. While the results in this paper are presented in the context of our prior research on pricing, the approach presented here applies to information extraction and implementation in other resource pricing approaches.