Abstract:
In social advertising, ads are targeted based on underlying social networks and their content is tailored with information that pertains to the social relationship. This paper explores the effectiveness of social advertising using data from field tests of different ads on Facebook. We find evidence that social advertising is effective, and that this efficacy seems to stem mainly from the ability of targeting based on social networks to uncover similarly responsive consumers. However, social advertising is \emph{less} effective if the advertiser explicitly states they are trying to promote social influence in the text of their ad. This suggests that advertisers must avoid being overt in their attempts to exploit social networks in their advertising.

Abstract:
Over the recent two decades substantial effort has been placed in expands our theoretical understanding of Analytics (machine learning, data mining, etc.). However, the necessity of a sound theoretical understanding of any analytical methodology is only a part of the overall vision. In thistalk we focus on the operationalisation of Analytics, that is how do we take these approaches and marry them into business requirements. What are the key ingredients to operationalise analytical methodologies? And why would one want to operationalise Analytics in the first place?

Abstract:
This paper focuses on contractual provisions in external sourcing of innovative services. Such agreements require protection of key knowledge assets, whilst simultaneously providing incentives for heuristic search to enable problem solving and knowledge transfer. We conceptualize two distinct dimensions of knowledge in the inter-organizational context: problem solving complexity and the need for synthesis of knowledge bases across organizational boundaries. Integrating explanations from transaction cost economics, incomplete contracting theory and knowledge-based research, we analyze a sample of IT outsourcing contracts to investigate the role of three contractual provisions: joint decision making rights, intellectual property safeguards, as well as the intensity of the incentives. Contracts for bilateral agreements that involve high problem solving complexity contain stronger and more clearly elucidated joint decision rights, which rely less on measurable outcomes, in conjunction with strong IP safeguards and low powered incentives. When prior experience of the vendor is critical for fulfillment of the contracted task, contracts include joint decision rights. Exchanges characterized by complementarity in knowledge bases across firms are more likely to be governed by high powered incentives contracts while exchanges characterized by co-specialization are governed by contracting arrangements involving joint decision rights in conjunction with low powered incentives.

Abstract:
It is possible for fingerprints from two different persons to be closely matched with each other. A spurious match such as this should be detected to avoid a false positive identification. Given a match between a fingerprint pair, we would like to quantify the extent of this match statistically. This is possible to do if the variability of the underlying processes are accounted for and modeled adequately. The extent of a match depends on two sources of variability: (1) inter-class variability arising from the spatial configuration of fingerprint features, and (2) intra-class variability arising from image quality, variability due to the fingertip placement on the sensor and elastic distortion of the skin. To adequately capture feature variability, we develop distributions on the feature space based on marked point processes that model clustering tendencies and spatial correlations between neighboring marks. Inference is carried out in a Bayesian MCMC framework. The proposed class of models is fitted to real fingerprint images to demonstrate the flexibility of fit to different kinds of fingerprint feature patterns arising in practice. Evidence of a Paired Impostor Correspondence (EPIC) is developed as a measure of fingerprint uniqueness, and its predictive value is obtained using simulation from the fitted models to quantify the extent of an observed match.

Abstract:
Abstract: Does information technology (IT) enable firms to globalize their operations and achieve higher foreign revenues and foreign profits? Although several studies have argued that IT can help firms globalize their operations, few studies have empirically tested this conjecture. We identify and discuss three mechanisms that explain why IT investments enable firms to globalize their operations – value chain coordination, value chain configuration, and local responsiveness. Using data on 259 multinational firms for an 8-year period (1999 – 2006), we find that aggregate IT investments are positively associated with higher levels of foreign revenues and lower levels of total costs. In turn, the increase in foreign revenues and reduction in total costs mediate profits from foreign operations. IT investments also help to increase domestic revenues and domestic profits. On the whole, we find that IT contributes to globalization both through higher revenues and lower total costs. These findings provide indirect evidence for the relative importance of underlying theoretical mechanisms that explain why IT helps firms to achieve higher foreign revenues and foreign profits, two key measures of a firm’s globalization. For example, results suggest that IT allows firms to increase foreign revenues through a local adaptation mechanism, and spread total costs over a larger revenues base through globalization efforts through value chain coordination and/or value chain configuration mechanisms. By documenting how IT creates value for firms through enabling globalization, we extend the business value of IT and international business literatures that have so far touched on firm-level globalization benefits from IT investments only in passing. This study is also important from a managerial perspective, because an understanding of how IT influences foreign revenues and foreign profits can help firms make appropriate changes in their IT strategies and IT investments to improve competitiveness. Key words: IT investments, globalization, multinational corporations, foreign revenues, foreign profits, value chain, coordination, configuration, responsiveness, business value of IT.

Abstract:
Consumer reviews are becoming increasingly important in consumers’ purchase decision process and in the success of new products. Prior literature acknowledges that consumer reviews impact product sales but are divided on the impact of the three metrics – valence, volume, and dispersion of consumer reviews – on product sales. The underlying thesis of the current paper is that these differences in findings are driven by the evolution of these metrics over time. Traditional models, which do not account for this evolution(shape) may fail to incorporate potentially vital information, and thereby lead to divergent conclusions. In this paper, using 395,297 online movie reviews collected from Yahoo and IMDB on 405 movies released from Feb. 1999 to Dec. 2010, we seek answers to the following questions: 1) Do consumer reviews matter i.e. can consumer reviews be used to predict box office sales? 2) How to quantify consumer reviews? Which metric – valence, volume or dispersion is more informative? 3) How do consumer reviews evolve? Does the shape of one metric influence the shape of another? Does their shape matters? We use functional data analysis to study the above questions, including the interactions among these eWOM matrics

Abstract:
In this paper we explore the role of related alliance experience, diversity in alliance experience, and information technology enabled knowledge integration capabilities in value creation in alliances. We conceptualize alliance experience in terms of relatedness and diversity and further identify the functional focus and industry of the partner as defining the nature of alliance experience. We theorize that both type-based and partner-industry based related experience will have a positive effect on alliance value. We also theorize that both type-based and partner-industry based diversity in alliance experience will have a positive effect on alliance value. Further, we theorize that while alliance experience enhances a firm’s knowledge, such knowledge has to be integrated, institutionalized and made accessible for it to be used effectively in future alliances. Hence firms with higher IT-enabled knowledge integration capabilities are more likely to do so and thereby enhance alliance value. Using data from 1030 alliances made by 89 firms across 11 industries, we test our research propositions. We find that type-based related experience is positively related to value gains in alliances whereas partner industry-based related experience affects alliance value negatively. We also find that a firm’s IT enabled knowledge integration positively moderates the effects of both related and diverse experience on value creation in alliances. Our findings highlight that while knowledge gained through learning by doing is important, complementary capabilities that enables firms to leverage and utilize such knowledge are also necessary for successful value creation in alliances. We interpret these findings and discuss their implications for research in both strategic management and information systems areas.

Abstract:
Many hospitals in the United States seek to lower their procurement costs by joining Group Purchasing Organizations (GPOs). GPOs operate as supply chain intermediaries - they do not buy or sell products; instead, they establish contracts with vendors on behalf of member hospitals. In a typical scenario, hospitals become members of a GPO; the GPO negotiates product prices with vendors on behalf of all its member hospitals in order to get deeper volume discount. However, there is evidence that some member hospitals further negotiate directly with the same vendors and contract at a price lower than the GPO price. Such contracts established directly between the same vendor and member hospitals are commonly known as custom contracts. The common perception is that hospitals benefit from these custom contracts because they yield lower prices. Using a game-theoretic model, we surprisingly find that exactly the opposite is true: the provision for custom contracts benefits vendors at the expense of hospitals. We also find that uncertainties in market prices largely drive the market outcomes. When the GPO shares indicative information on market price with its member hospitals in an effort to better educate them, it increases not only the social surplus but also the profitability of the GPO vendor. Our research makes significant contribution towards the literature on group buying as well as intermediaries - we show how with the provision for custom contracts, GPOs expectedly act as demand aggregators for relatively small hospitals, while for the rest, they unexpectedly play the role of information intermediaries. We drew our example from the healthcare sector; however, our results can be applied to any other industry in the context of group purchasing.

Abstract:
We develop an economic model that examines whether a vendor or client should own the assets underlying service delivery in an IT outsourcing relationship. Prior research has argued that the vendor should own the assets to provide incentives for investment on the production assets. We allow for investment in innovation that can benefit both client and vendor in the relationship. Our model offers an explanation for the empirically observed heterogeneity in asset ownership structure using Incomplete Contracts Theory. We find that optimal ownership structure can vary due to the different incentives of the client and vendor and differences in the available set of investment opportunities. Interestingly, we find that scenarios exist where both the client and vendor agree on the ownership structure and where they disagree. When investment in innovation enables new services and features, the client should retain ownership of the assets. When investment leads to cost savings, the vendor should own the assets. The parties disagree on asset ownership structure when the investment opportunities yield similar levels of benefits to both vendor and client. In this case neither party wants to own the assets. When there are multiple investment opportunities with payoffs in new services and in cost reduction, both client and vendor prefer to own the assets. We extend the model to allow renegotiation and find, counter-intuitively, that when renegotiation is allowed the parties always disagree on the ownership structure. In contrast, in the absence of renegotiation, the client and vendor agree on the ownership structure in most cases. We find that allowing renegotiation leads to greater investment in innovation but also results in more gaming between the parties with ex post surplus extraction and more free-riding.

Abstract:
Algorithmically matching items to users in a given context is essential for the success and profitability of large scale recommender systems like content optimization, computational advertising, web search, shopping, movie recommendation and so on. Developing such match-making algorithms is a new scientific sub-discipline that involves strong interactions among several disciplines like computer science, statistics, machine learning, economics, optimization. This talk will discuss mathematical formulations, the progress made, and the big challenges that lie ahead. Throughout, I will use examples from real-world recommender systems in content optimization and computational advertising at Yahoo!

Abstract:
Recent papers have shown that, in contrast to ―the Long Tail‖ theory, movie sales remain concentrated in a small number of hits. These papers have argued that concentrated sales can be explained, in part, by he-terogeneity in quality and increasing returns from social effects. Our research analyzes an additional ex-planation: how incomplete information may skew sales patterns. We use the movie broadcast on pay-cable channels as an exogenous shock to the availability of information, and analyze how this shock changes the resulting sales distribution. Our data show that the pay-cable broadcast shifts the distribution of DVD sales toward ―Long Tail‖ mov-ies, suggesting an information spillover from the broadcast. We further develop a learning-based model of DVD demand to precisely quantify the lost DVD sales due to incomplete information. Our study contri-butes to the academic literatures on information provision and market outcomes, and the dynamics of long tail markets. Keywords: Incomplete information, product discovery, multichannel distribution, movie indus-try, learning model, Long Tail.

Abstract:
Internet retailers have been making significant investments in advanced technologies, e.g., zoom, color swatch, and alternative photos, that are capable of providing detailed product-related information and, thereby, mitigating the lack of “touch-and-feel,” which, in turn, is expected to lower product returns. However, a clear understanding of the impact of these technologies on product returns is still lacking. This study attempts to fill this gap by using several econometric models to unravel the relationship between product-related technology usage and product returns. Our unique and rich dataset allows us to measure technology usage at the product level for each consumer. The results show that zoom usage has a negative and weakly significant coefficient, suggesting that a higher use of the zoom technology leads to fewer returns. Color swatch, on the other hand, does not seem to have any impact on product returns. Interestingly, we find that the use of alternative photos increases the likelihood of returns. Thus, our findings show that different technologies have different effects on product returns. Moreover, with a higher use of alternative photos, loyal consumers are more likely to return, whereas the effect on non-loyal consumers is insignificant. We provide explanations for all these findings based on the extant literature on customer satisfaction. We also conduct a number of tests to ensure the robustness of the findings.

Abstract:
A two-sided market describes a situation in which a “platform” facilitates an economic transaction between two distinct types of market participants. A key aspect of any two-sided market is the presence of cross-type and possibly intra-type network externalities under the influence of the platform. The extant two-sided market literature has focused primarily on the platform pricing decision. This article posits that frictional aspects of two sided markets that facilitate matching are also extremely important and the tremendous flexibility that Internet-enabled two-sided markets have in market design gives the platform a significant opportunity to influence these frictions.This article theoretically and empirically examines the impact of a platform design feature on the equilibrium behavior of a two sided market that enables person-to-person (P2P) or peer-to-peer loans. More specifically I examine how introducing pre-defined automated bidding agents to a P2P lending market affect prices, volume, and welfare. I provide theoretical analyses showing that introducing such automated agents can help the market clear each period, reduce price dispersion, and increase total market surplus. Interestingly, it is possible for the introduction of these agents, which may help lenders extract surplus form borrowers, to simultaneously drive some borrowers out of the market and increase total market volume and surplus generation. This apparent contradiction is possible because there is an increase in the successful matching of borrowers who remain in the market. The empirical section looks at the actual impact of introducing pre-defined bidding agents to the Prosper.com P2PL marketplace. Mirroring the theoretical predictions, there is evidence of increased loan originations in submarkets where the bidding agents are active. The agents can also be linked to a decrease in price dispersion further suggesting the bidding agents enhanced market efficiency.

Abstract:
Measuring the causal effects of online advertising (adfx) on user behavior is important to the health of the WWW publishing industry. In this paper, using three controlled experiments, we show that observational data frequently lead to incorrect estimates of adfx. The reason, which we label "activity bias," comes from the surprising amount of time-based correlation between the myriad activities that users undertake online. In Experiment 1, we track account sign-ups at a competitor's (of the advertiser) website and find that most people sign-up the day they saw an advertisement, but that the true "competitive effect" was minimal. In Experiment 2, users that are exposed to an ad on a given day are much more likely to engage in brand-relevant search queries as compared to their recent history for reasons that had nothing do with the advertisement. In Experiment 3, we show that activity bias occurs for page views across diverse websites. In all three experiments, exposure to a campaign signals doing "more of everything" in given period of time, making it difficult to find a suitable "matched control" using prior behavior. In such cases, the "match" is fundamentally different from the exposed group, and we show how and why observational methods lead to a massive overestimate of adfx in such circumstances.

Abstract:
Many industries feature a vertical distribution channel structure in which a downstream player (retailer) sells a bundle, composed of products from multiple upstream producers (manufacturers). For example, cable or satellite TV carriers bundle dozens of channels from multiple studios and programming networks. The bundling literature oers deep insights about the economic benets of bundling, but is limited to a direct manufacturer-buyer set- ting. Conversely, marketing and supply chain studies on channel management have not con- sidered the possibility of product bundling by the retailer. How do the economic incentives of the manufacturers and retailers in a vertical channel impact the mechanics of bundling? We develop a unique two-stage model to capture the competitive dynamics between the upstream manufacturers and the downstream retailer, which combines the horizontal competition be- tween manufacturers with the strategic choice of selling mechanism (bundle or components) by the retailer. We show that bundling need not emerge as an equilibrium outcome in a vertical channel, even under conditions ripe for its prevalence in a direct manufacturer-buyer market. If the retailer were forced to bundle, the manufacturers exploit this restriction and over-price their components, leading to substantial economic losses especially to the retailer. The retailer's threat to unbundle the products does not negate these losses, rather it veers the system to a component-selling equilibrium. Despite this failure of a bundling equilib- rium, bundling has the Pareto-improving property of raising the surplus of all rms and the collective consumer surplus. Bundling outcomes may, therefore, yet emerge due to alterna- tive industry dynamics. However, these outcomes will be subject to constant pressure due to individual rms' self-interest and desire to grab a greater share of the gains from bundling, as indeed is frequently witnessed in the form of \carriage disputes" within the TV industry. Other mechanisms must therefore be sought that lead to bundling as a natural competitive outcome.

Abstract:
The purpose of our work is to highlight the need for integrating predictive analytics into information systems (IS) research. Predictive analytics include empirical methods (statistical and other) that generate data predictions as well as methods for assessing predictive power. Predictive analytics not only assist in creating practically useful models, they also play an important role alongside explanatory modeling in theory building and theory testing. We describe six roles for predictive analytics: new theory generation, measurement development, comparison of competing theories, improvement of existing models, relevance assessment, and assessment of the predictability of empirical phenomena. Despite the importance of predictive analytics, we find that they are rare in the empirical IS literature. The latter relies nearly exclusively on explanatory statistical modeling, where statistical inference is used to test and evaluate the explanatory power of underlying causal models. However, explanatory power does not imply predictive power and thus predictive analytics are necessary for assessing predictive power and for building empirical models that predict well. To show the distinction between predictive analytics and explanatory statistical modeling, we present differences that arise in the modeling process of each type. These differences translate into different final models, so that a pure explanatory statistical model is best tuned for testing causal hypotheses and a pure empirical predictive model is best in terms of predictive power.

Abstract:
Websites typically provide several links on each page visited by a user. Some of these links help users easily navigate the site. Some others are used to provide targeted recommendations based on the available user profile. When the user profile is not adequate, the site cannot effectively target products, promotions and advertisements; these links are then often determined in a generic manner (e.g., non-personalized links that are determined based on other considerations such as overall popularity, etc.). Naturally, the faster the site can learn a user’s profile, the sooner the site can start benefiting from personalization. We study how a site can learn the profile of a user (with inadequate profile) as the user traverses the site. We develop a technique that sites can use to learn (or improve) as quickly as possible the profile of such visitors. The technique identifies links to make available that will lead to a more informative profile when the user chooses one of the offered links. We also present a heuristic approach that is computationally efficient when the number of possible links to consider is very large. We conduct simulated experiments to compare the heuristic approach with a benchmark approach that selects links based on popularity, varying the proportion of links that are determined based on popularity and those offered in order to learn a user’s profile. We find that the proposed approach learns the profiles markedly better after very few user interactions compared to the benchmark approach, even when a small proportion of links are used to learn profiles

Abstract:
Extant research suggests that tacit collusion or the “golden rule” of refraining from aggressive pricing in jointly contested markets is an integral feature of the US airline industry. Our research revisits this past wisdom in the presence of airlines that pursue a distinctly different pricing strategy. Amongst airlines, Southwest and JetBlue largely practice an Everyday Low Price (EDLP) format which can be best characterized as a portfolio-level pricing strategy while most others engage in some form of temporal fare promotions. In a first study using both posted and transacted airline prices, we examine the impact of asymmetric pricing strategies on the understanding of multimarket contact (MMC). By first ignoring these differences in pricing strategy, we are able to replicate extant results, i.e. the golden rule appears to be followed throughout this sector even today and in electronic markets. We then separately identify multimarket contact of a focal firm with like and asymmetric firms and develop both carrier-route and route-level measures of MMC. Our results confirm that the impact of MMC on prices is rendered insignificant (or less significant) when accounting for the presence of this alternative pricing strategy. Subsequently we analyze MMC with similar non-EDLP carriers and those with EDLP carriers. Our findings show that while tacit collusion appears to take place when MMC occurs between non-EDLP carriers, MMC with EDLP carriers actually lowers prices in the marketplace. Further, it also appears that EDLP airlines avoid MMC with each other. Our findings argue that any tacit collusion from mutual forbearance is a possibility only when both firms in contact are likely to employ price changes; when there is asymmetry in the pricing rationale there is no scope for tacit collusion. We conclude with a call to include such asymmetries in pricing mechanisms for other industry contexts as well in newer game-theoretic formulations of MMC.

Abstract:
We consider a subscription based DVD rental organization, such as Netflix and Blockbuster, where the satisfaction of a customer depends on the availability of the movie he/she requests. Hence, it is important for the firm to satisfy the demands of as many customers as possible. Recommender systems are increasingly being used by these firms to help customers in finding the right movie. However, these firms may also use the recommendation system as a tool to influence the demand of the customers based on the inventory status. In this research, we address this issue by optimizing the recommendation of movies to a customer based on the existing inventory as well as the expected future demand and return patterns, with the objective of maximizing the customer satisfaction. We begin with presenting a mixed integer programming formulation for this problem. Then, we present several analytical results and useful managerial insights. Next, we show that the problem is strongly NP-hard, and propose an efficient heuristic which provides the near-optimal solution for a wide variety of problem instances. This heuristic is then extended to solve a dynamic problem where the recommender system adapts dynamically based on the actual customer demands and returns. We also present several useful experimental results that could help organizations in designing more intelligent recommender systems as well as optimizing the inventory level. Our analytical and experimental results indicate that consideration of inventory status in the recommendation system may significantly improve the objective of the DVD rental organization.

Abstract:
Observation and analysis of a phenomenon at unprecedented levels of granularity not only furthers our understanding of it, but also transforms the way it is studied. For instance, invention of gene-sequencing and computational analysis transformed the life sciences, creating fields of inquiry such as genomics, proteomics, etc.; and the Hubble space telescope has furthered the ability of humanity to look much farther beyond what we could otherwise. With the mass adoption of the Internet in our daily lives, and the ability to capture high resolution data on its use, we are at the threshold of a fundamental shift not only in our understanding of the social and behavioral sciences, but also the ways in which we study them. Social Games (of the type available on sites such as FaceBook), Massively Multiplayer Online Games (MMOGs) and Virtual Worlds (VWs) have become increasingly popular and have communities comprising tens of millions. They serve as unprecedented tools to theorize and empirically model the social and behavioral dynamics of individuals, groups, and networks within large communities. This talk introduces the Virtual World Exploratorium, a multi-institutional (University of Minnesota Computer Science, Northwestern U Kellog School of Management, University of Chicago School of Business, USC Annenberg School of Communication, and University of Illinois Sociology and Communications), multi-disciplinary project which uses data from commercial MMOGs and VWs to study many fields of social science, including sociology, social psychology, organization theory, group dynamics, macro-economics, etc. This talk summarizes findings from many of these disciplines. Given the amount of data being generated (e.g. all of Zynga's games on Facebook generate around 3 terabytes of data a day), there are exciting new challenges for the computer science community, especially in the areas of data management, data mining, and algorithms. We also present some findings in these areas. This research is financially supported by the NSF, the Army Research Institute, the IARPA, and by the ARL through a Network ScienceCTA award. Sony Online Entertainment and Linden Labs have provided the dataset for this research

Abstract:
Many networks have attained a large user base induced by strong network effects. Such positive externalities are thought to create an “economic moat” by increasing switching costs, thus protecting the incumbent network against new entrants. However, the underlying assumption behind this result is that users choose to be on only one network at any given time. Does the strength of the “economic moat” hold up when users can to co-exist on multiple networks? We develop an analytical model of network adoption in a setting where a new entrant arrives with superior capabilities, and where users derive value from both network effects and intrinsic capabilities of a network. We measure adoption by the amount of time spent on a network rather than a binary variable implying zero or full adoption, and demonstrate through a multi-period model that the “moat” created by network effects for the case of incremental adoption is weaker than the case of complete adoption. Conversely, a new entrant can start a “bleeding” effect with a marginally superior capability relative to the incumbent, which can then culminate into an avalanche in later periods. Additional results involve the temporal dynamics of incremental migration to the new network, which are of interest to both the incumbent and new entrant in terms of strategies to improve their intrinsic capabilities. The results of the study can help explain migration patterns observed in online social networks, where early movers like MySpace and Orkut have lost significant ground to latecomers like Facebook and Twitter.

Abstract:
Many firms such as FedEx and Wal-Mart have invested in Information Technologies such as package tracking systems, inventory management systems and Enterprise Resource Planning (ERP) systems to reduce their costs. We study the role of uncertainty and intensity of industry competition on the amount of investment in IT and the returns from such investment. We develop two duopoly models -- one where the firms determine their investment amount simultaneously and another where they do so sequentially. We find that in the simultaneous move game, an increase in the intensity of competition leads to lower profits yet the firms increase their investment in IT. This result holds when the success of the IT investment is uncertain. In the absence of this uncertainty, the amount of investment is unaffected by changes in the intensity of competition. The investment amount is lower when there is greater uncertainty but the firms' profits are higher. In the sequential move game, we find that both the leader and the follower invest in IT when the intensity of competition is not too high. While the leader invests more with increasing intensity of competition, the follower invests more when uncertainty is sufficiently high. When there is sufficiently low uncertainty about the success of the IT investment, increasing intensity of competition causes the follower to reduce his investment in IT. The leader invests more than the follower and earns larger profits, however increasing uncertainty about the success of the IT investment reduces the leader's first mover advantage. In contrast to prior analytical literature we show that in a sequential move game, the leader invests more in IT in the sequential move game than the simultaneous move game. The follower invests less than in the simultaneous move game. We also examine the return ratio and the coefficient of variation and find that the coefficient of variation of the follower is often greater than that of the leader and our other results are consistent with prior empirical literature.

Abstract:
We study the online market for peer-to-peer (P2P) lending, in which individuals bid on unsecured microloans sought by other individual borrowers. Using a large sample of consummated and failed listings from the largest online P2P lending marketplace - Prosper.com, we test whether social networks lead to better lending outcomes, focusing on the distinction between the structural and relational aspects of networks. While the structural aspects have limited to no significance, the relational aspects are consistently significant predictors of lending outcomes, with a striking gradation based on the verifiability and visibility of a borrower's social capital. Stronger and more verifiable relational network measures are associated with a higher likelihood of a loan being funded, a lower risk of default, and lower interest rates. We discuss the implications of our findings for financial disintermediation and the design of decentralized electronic lending markets. (This is a joint seminar between Information Systems and Econ-Finance areas)

Abstract:
In this paper, we analyze the cost allocation problem when a group of agents or nodes have to be connected to a source, and where the cost matrix describing the cost of connecting each pair of agents is not necessarily symmetric, thus extending the well-studied problem of minimum cost spanning tree games, where the costs are assumed to be symmetric. An arborescence is a minimal set of edges such that every node is connected to the source. Our objective is to come up with rules to share the cost of minimum cost arborescence. The problem has many applications: An example is a set of villages which need to be connected to a reservoir for water. A village may get water directly from the reservoir or via another village. The cost of transporting water from one village to another may not be symmetric because of altitude and other reasons. How should the villages share the total cost? The symmetric version of this problem is well-studied. We show that while some results are similar, there are also significant differences between the frameworks corresponding to symmetric and asymmetric cost matrices. Our focus is on rules which satisfy axioms representing incentive and fairness properties. Our first result is that if we impose core selection and an independence of irrelevant costs (IIC) axiom, then it almost pins down a unique rule. This is a strong departure from the symmetric version of the problem, where there are many rules which satisfy the counterparts of these two axioms. Our second result is that any rule which satisfies core selection and IIC cannot be continuous and/or monotonic (in edge costs). This is again in sharp contrast with the rules defined for symmetric version of the problem. Finally, we define a rule which satisfies core selection, continuity, monotonicity, and some equity axioms. This rule is based on the idea of defining an irreducible cost matrix for every cost matrix, and then showing that such an irreducible cost matrix gives rise to a convex cooperative game. Our rule is the Shapley value of this new cooperative game.

Abstract:
Researchers in competitive dynamics area have demonstrated that firms that carry out intense, complex and heterogeneous competitive actions exhibit better performance. However, there is a need to understand factors that enable firms to undertake competitive actions. In this study, we focus on two antecedents of competitive behavior of firms: 1) access to network resources and 2) use of information technology. We argue that while network structure provides firms with the opportunity to tap into external resources, the extent to which they are actually exploited depends on firms' IT capability. We develop a theoretical model that examines the relationships between IT capability, network structure, and competitive action. We test the model using secondary data about 12 major automakers over 16 years from 1988-2003. We find that both sparse network structure with many brokerage opportunities and dense network structure have a positive effect on the future ability of firms to introduce a greater number and wider range of competitive actions. However, whereas sparse network structure has a direct positive effect on action volume and complexity, the effect of dense network structure is contingent on the firm’s IT capability. Firms benefit from dense network structure only when they develop strong IT capability. Our results suggest that IT capability plays both substitutive role, when firms do not have advantageous access to brokerage opportunities, and complementary role, when firms are embedded in a dense network structure, in the relationship between network structure and competitive actions

Abstract:
Firms can price discriminate by inducing consumers to self-select from a large menu of options, or by including additional instruments in the pricing scheme. We show that the latter approach is better when tariff management costs increase with tariff size. Specifically, comparing two-part tariffs (2PTs) with three-part tariffs (3PTs), we find that a relatively small menu of 3PTs (which use an additional instrument, the \free allowance") can be more profitable than a menu of 2PTs of any size. Often, a single three-part tariff can beat the best-possible menu of two-part tariffs. Moreover, this 3PT menu can be designed with less information about consumer preferences, relative to the menu of two-part tariffs which, in order to segment customers optimally, needs fine-grained information about preferences. Our analysis reveals a counterintuitive insight that more complex tariffs need not always be more profitable, it matters whether the complexity is from many choices or more pricing instruments.

Abstract:
To present a formal model for service delivery which can be used to describe a service delivery system and generate simulation models. These simulation models are at the core of an ongoing effort to optimize the workforce delivering IT support services in IBM's Global Delivery Centers. A pilot in the Bangalore and Hyderabad delivery centers is already running. The trials and tribulations involved in taking elegant theories into practice will be discussed.

Abstract:
Managers focus upon innovation and efficiency as two alternative means to improving their firm’s performance. Information systems research has demonstrated that investments in information technology usually contribute to the improvements in organizational efficiency. Other research has sought to examine whether information technology investments enhance organizational innovativeness. Yet, a common feature of most prior research is a universalistic assumption, viz., that IT investments will always improve efficiency or innovation, regardless of the firms’ environmental context. To extend prior research, we propose a contingency theory explanation and propose that the dynamism, munificence, and complexity of the environment in which firms’ operate moderates the relationship between IT investments and efficiency and innovation outcomes. Using panel data from 2003 to 2005 that covers a wide range of industry environments, our research finds that in more stable environments (lower levels of dynamism, munificence and complexity), IT investments are associated with a greater improvement in the efficiency of operations. However, in more unstable environments (higher levels of dynamism, munificence and complexity), IT is associated with a greater increase in innovation (i.e., development of new products and processes, and exploration of growth opportunities). Thus, our analysis suggests that IT is associated with distinct value-creation processes and benefits, depending on the characteristics of the firms’ environment.

Abstract:
Although the rapid growth of online applications via the internet offers convenience for customers, it has excessively increased the volume of erroneous data. Problems such as poor interface design, limited typing skills, language deficiencies or deliberate falsification of data all lead to errors in the input supplied by the applicant. In this paper, we analyze identity risk due to inaccurate or incomplete information about an entity or a person and the ensuing financial implications for businesses. We first study how to estimate an optimal threshold level for a given black box matching algorithm when restricted by only a small sample of training data. We propose different threshold estimators and analyze their asymptotic properties. Next, we explore the option of procuring additional information to help improve accuracy of record matches. We develop a model to compute two threshold levels between which it is optimal to exercise the option of buying additional information. Lastly, we present numerical results demonstrating the properties of the threshold estimators and the properties of the model with the option to procure additional information.

Abstract:
Two problems in Information Systems Design that require a graph-theoretic approach will be presented. The first problem, referred to as the Maximum Commonality Assignment Problem (MCAP), arises in the context of Extreme Programming (XP) while assigning developers to modules in a software project. In common XP practice, a pair of developers is assigned to a single module. This pair can potentially be split, i.e., a developer may be paired with more than one other developer to work on different modules in the project. While the practice of pair splitting could result in knowledge dissemination benefits, it requires a developer to adjust to the coding practices of several other developers. We examine the benefits of pair splitting under several specific graphs (e.g., a tree, a bi-partite network, etc.). The second problem, referred to as the Structured Search Problem (SSP), arises in the context of search in Social Networks. While searching in a social network, the end user often requires a subset of nodes within the network that optimises a set measure. The nature of search in a social network is therefore more complex than search on the world-wide-web. Here two specific search problems: the Elite Group Problem and the Portal Problem will be presented. Opportunities for further research will be discussed.