Knowledge that Transforms

Technological advances enable sellers to price discriminate based on a customer's revealed purchasing intentions. E-tailers can track items in online shopping carts and radio frequency identification tags enable retailers to do the same in brick-and-mortar stores. To leverage this information, it is important to understand how this new visibility impacts pricing and market outcomes. We propose a model in which a seller sets prices for goods A and B, allowing for the possibility of sequentially revising the price for good B if the buyer reveals a preference for good A by making an initial purchase decision. We derive comparative statics results for the prices of products that have superadditive or subadditive values, and also for the associated profits. We also run simulations for a range of distributions of buyer values, to compare sequential pricing with mixed bundling. The results indicate that information technology-enabled sequential pricing can increase profits relative to mixed bundling or pure components pricing for substitute goods due to a reduction of intraseller competition. We also consider the case of goods with positively or negatively correlated values and find that when sellers can condition the second good's price on the buyer's decision to purchase the first good, sequential pricing increases profits when customer's values for the goods are highly positively correlated.

Extant research is equivocal about the organizational performance effects of customer relationship management (CRM) technology use, with some studies reporting positive effects and other studies reporting no effects at all. The present research effort posits that these mixed findings may potentially be explained by two factors: (1) CRM technology use may have different effects on different customers, and (2) different CRM tools may have different performance consequences. This study investigates this possibility by building on relationship marketing and management theory to propose and test a model of the customer- and firm-level consequences of the organizational use of CRM interaction support and customer prioritization tools. The results of data analysis of 295 customer firms nested within 10 provider firms reveal that firm use of CRM interaction support tools is positively related to customers' relationship perceptions, regardless of customer account size. In contrast, the data indicate that use of CRM prioritization tools appears to have positive effects on a firm's larger customers and negative effects on smaller customers. The results also suggest that when considered at an aggregate level, customer perceptions of the exchange relationship are predictive of organizational performance and that the association between these two variables is significant for larger customer accounts but insignificant for smaller accounts. Overall, the study's results help explain some of the inconsistent findings reported in the literature regarding the performance implications of CRM technology use and suggest that use of the technology may serve to enhance organizational performance, at least over the short term.

Dealing with spam is very costly, and many organizations have tried to reduce spam-related costs by installing spam filters. Relying on modern econometric methods to reduce the selection bias of installing a spam filter, we use a unique data setting implemented at a German university to measure the costs associated with spam and the costs savings of spam filters. Our methodological framework accounts for effect heterogeneity and can be easily used to estimate the effect of other IS technologies implemented in organizations. The majority of costs stem from the time that employees spend identifying and deleting spam, amounting to an average of approximately five minutes per employee per day. Our analysis, which accounts for selection bias, finds that the installation of a spam filter reduces these costs by roughly one third. Failing to account for the selection bias would lead to a result that suggests that installing a spam filter does not reduce working time losses. However, cost savings only occur when the spam burden is high, indicating that spam filters do not necessarily reduce costs and are therefore no universal remedy. The analysis further shows that spam filters alone are a countermeasure against spam that exhibits only limited effectiveness because they only reduce costs by one third.

As the ability to measure technology resource usage gets easier with increased connectivity, the question whether a technology resource should be priced by the amount of the resource used or by the particular use of the resource has become increasingly important. We examine this issue in the context of pricing of wireless services: should the price be based on the service, e.g., voice, multimedia messages, short messages, or should it be based on the traffic generated? Many consumer advocates oppose discriminatory pricing across services believing that it enriches carriers at the expense of consumers. The opposition to discrimination has grown significantly, and it has even prompted the U.S. Congress to question executives of some of the biggest carriers. With this ongoing debate on discrimination in mind, we compare two pricing regimes here. One regime, namely, service pricing, involves pricing different services differently. The other one, namely, traffic pricing, involves pricing the traffic (i.e., bytes) transmitted. We show why the common wisdom, that discriminatory pricing across services increases profits and harms consumers, may not always hold. We also show that such discrimination can increase social welfare.

Combinatorial auctions are used in a variety of application domains, such as transportation or industrial procurement, using a variety of bidding languages and different allocation constraints. This flexibility in the bidding languages and the allocation constraints is essential in these domains but has not been considered in the theoretical literature so far. In this paper, we analyze different pricing rules for ascending combinatorial auctions that allow for such flexibility: winning levels and deadness levels. We determine the computational complexity of these pricing rules and show that deadness levels actually satisfy an ex post equilibrium, whereas winning levels do not allow for a strong game theoretical solution concept. We investigate the relationship of deadness levels and the simple price update rules used in efficient ascending combinatorial auction formats. We show that ascending combinatorial auctions with deadness level pricing rules maintain a strong game theoretical solution concept and reduce the number of bids and rounds required at the expense of higher computational effort. The calculation of exact deadness levels is a [Formula: see text]-complete problem. Nevertheless, numerical experiments show that for mid-sized auctions this is a feasible approach. The paper provides a foundation for allocation constraints in combinatorial auctions and a theoretical framework for recent Information Systems contributions in this field.

When companies purchase information technology (IT) products for their employees, departments, or divisions, whether to standardize on one product or to allow the users to make their own choices is an important decision for IT managers to make. By consolidating demand and committing to buy from a single seller, standardization ensures product compatibility within the corporation and has a potential to induce intense price competition among sellers, but this potential is subject to whether competing products are compatible and the relative competitive advantages of the sellers. This paper studies when it is optimal for an employer to commit to exclusive purchase from a single seller to enforce standardization and sellers' incentives to invest in mutual compatibility. Our results suggest that the employer is more likely to make such a commitment when the competing products are compatible, less vertically differentiated, and/or more horizontally differentiated. We also find that the sellers agree to cooperate and invest in mutual compatibility only when the gap between their competitive advantages is moderate, but the availability of third party converters that enable partial compatibility can induce more collaboration among the sellers.

Combinatorial auctions have been suggested as a means to raise efficiency in multi-item negotiations with complementarities among goods because they can be applied in procurement, energy markets, transportation, and the sale of spectrum auctions. The combinatorial clock (CC) auction has become very popular in these markets for its simplicity and for its highly usable price discovery, derived by the use of linear prices. Unfortunately, no equilibrium bidding strategies are known. Given the importance of the CC auction in the field, it is highly desirable to understand whether there are efficient versions of the CC auction providing a strong game theoretical solution concept. So far, equilibrium strategies have only been found for combinatorial auctions with nonlinear and personalized prices for very restricted sets of bidder valuations. We introduce an extension of the CC auction, the CC+ auction, and show that it actually leads to efficient outcomes in an ex post equilibrium for general valuations with only linear ask prices. We also provide a theoretical analysis on the worst case efficiency of the CC auction, which highlights situations in which the CC leads to highly inefficient outcomes. As in other theoretical models of combinatorial auctions, bidders in the field might not be able to follow the equilibrium strategies suggested by the game-theoretical predictions. Therefore, we complement the theoretical findings with results from computational and laboratory experiments using realistic value models. The experimental results illustrate that the CC+ auction can have a significant impact on efficiency compared to the CC auction.

This article investigates the economic consequences of data errors in the information flows associated with business processes. We develop a process modeling-based methodology for managing the risks associated with such data errors. Our method focuses on the topological structure of a process and takes into account its effect on error propagation and risk mitigation using both expected loss and conditional value-at-risk risk measures. Using this method, optimal strategies can be designed for control resource allocation to manage risk in a business process. Our work contributes to the literature on both ex ante risk management-based business process design and ex post risk assessments of existing business processes and control models. This research applies not only to the literature on and practice of process design and risk management but also to business decision support systems in general. An order-fulfillment process of an online pharmacy is used to illustrate the methodology.

Despite many success stories, B2B e-commerce penetration remains low. Many firms introduce electronic channels in addition to their traditional sales channels but find that buyer usage of the e-channel over time does not keep up with initial expectations. Firms must understand the underlying factors that drive channel usage and how these factors change over time and across buyers. Using panel data pertaining to the purchase histories of 683 buyers over a 43-month period, we estimate a dynamic discrete choice model in a B2B setting that (i) recognizes how price, channel inertia, and inventory change over time; (ii) allows buyers to dynamically trade off these factors when making e-channel adoption decisions; and (iii) takes into account buyer heterogeneity. We find that channel usage is both heterogeneous and dynamic across buyers. Our findings reveal the dynamic tradeoff between channel inertia and the adverse price effect, which interact in opposing directions as the e-channel grows more popular over time: price increases resulting from more bids deter buyers, whereas channel inertia built from sampling experience helps retain repeat buyers for the new channel. Second, we find that the buyers' size and diversity influence purchase decisions, and the e-channel appears more attractive to small and/or diversified buyers. Based on our analysis, we postulate that the seller's allocation decisions of products across channels, if not aligned with buyer behavior, can alienate some buyers. Based on the parameter estimates from the buyer response model, we propose an improved channel allocation that enables firms to selectively attract more buyers to the e-channel and improve revenues. Channel acceptance increases as a result of smart allocation when firms understand and account for individual buyers' channel usage behavior.

In this paper we explore the underlying consumer heterogeneity in competitive markets for subscription-based information technology services that exhibit network effects. Insights into consumer heterogeneity with respect to a given service are paramount in forecasting future subscriptions, understanding the impact of price and information dissemination on market penetration growth, and predicting the adoption path for complementary products that target the same customers as the original service. Employing a continuous-time utility model, we capture the behavior of a continuum of consumers who are differentiated by their intrinsic valuations from using the service. We study service subscription patterns under both perfect and imperfect information dissemination. In each case, we first specify the conditions under which consumer rational behavior supported by the utility model can explain a general observed adoption path, and if so, we explicitly derive the analytical closed-form expression for the consumer valuation distribution. We further explore the impact of awareness and distribution skewness on adoption. In particular, we highlight the practical forecasting importance of understanding the information dissemination process in the market as observed past adoption may be explained by several distinct awareness and heterogeneity scenarios that may lead to divergent adoption paths in the future. Moreover, we show that in the later part of the service lifecycle the subscription decision for new customers can be driven predominantly by information dissemination instead of further price markdowns. We also extend our results to time-varying consumer valuation scenarios. Furthermore, based on our framework, we advance a set of heuristic methods to be applied to discrete-time real industry data for estimation and forecasting purposes. In an empirical exercise, we apply our methodology to the Japanese mobile voice services market and provide relevant managerial insights from the analysis.