A key trend in the world—especially in electronic commerce—is a demand for higher levels of expressiveness in the mechanisms that mediate interactions, such as the allocation of resources, matching of peers, and elicitation of opinions from large and diverse communities. Intuitively, one would think that this increase in expressiveness would lead to more efficient mechanisms (e.g., due to better matching of supply and demand). However, until now we have lacked a general way of characterizing the expressiveness of these mechanisms, analyzing how it impacts the actions taken by rational agents—and ultimately the outcome of the mechanism. In this technical report we introduce a general model of expressiveness for mechanisms. Our model is based on a new measure which we refer to as the maximum impact dimension. The measure captures the number of different ways that an agent can impact the outcome of a mechanism. We proceed to uncover a fundamental connection between this measure and the concept of shattering from computational learning theory. We also provide a way to determine an upper bound on the expected efficiency of any mechanism under its most efficient Nash equilibrium which, remarkably, depends only on the mechanism’s expressiveness. We show that for any setting and any prior over agent preferences, the

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Mechanisms are present everywhere in both business and social contexts. They govern the interactions people have with businesses, governments, and each other. One emerging trend over the past decade is a demand for higher levels of expressiveness in mechanisms that mediate interactions such as the allocation of resources, matching of peers, and elicitation of opinions. This trend has already manifested itself in combinatorial auctions and generalizations thereof. It is also reflected in the richness of preference expression offered by businesses as diverse as matchmaking sites, sites like Amazon and Netflix, and services like Google’s AdSense. In Web 2.0 parlance, this demand for increasingly diverse offerings is called the Long Tail. A driving force behind this trend is that greater expressiveness begets better matches, or greater efficiency of the outcomes. Yet, expressiveness does not come for free; it burdens users to specify more preference information. Today’s mechanisms have relied on empirical tweaking to determine how to deal with this and related tradeoffs. In this thesis, we propose to establish the foundations of expressiveness in mechanisms and its relationship to their efficiency,

Abstract—This position paper examines the development of a dedicated service aggregator role in business networks. We predict that these intermediaries will soon emerge in service ecosystems and add value through the application of dedicated domain knowledge in the process of creating new, innovative services or service bundles based on the aggregation, composition, integration or orchestration of existing services procured from different service providers in the service ecosystem. We discuss general foundations of service aggregators and present Fourth-Party Logistics Providers as a real-world example of emerging business service aggregators. We also point out a demand for future research, e.g. into governance models, risk management tools, service portfolio management approaches and service bundling techniques, to be able to better understand core determinants of competitiveness and success of service aggregators.

To offer online bundles of independent e-services, software is needed that composes services of different enterprises into a bundle of services, satisfying a complex consumer need. In earlier research, a service ontology – a formalized conceptual model of services – has been developed for such software-aided service bundling. This ontology, however, did not include constructs to reason about the prices of services, while these are needed to realize and offer service bundles online. In this paper, we present an extension of the service ontology with pricing models for e-services. Examples from real-world services are used to illustrate how pricing models of services can be modelled by domain experts, and prices of service bundles can be calculated by software using the extended ontology.

We thank Marriott International for providing financial support and data and fine.com for assistance with online data collection. We also thank the participants at the Marketing Science Conference on Internet at Boston for their helpful comments. 2 Customer Price Sensitivity and the Online Medium The nature of the online medium and the characteristics of web sites can significantly alter the degree of customer price sensitivity in online markets. Many managers fear that the Internet will increase price sensitivity and intensify price competition. There is, however, very little conceptual or empirical research on this topic. In this paper, we use the information search literature to develop a conceptual framework comprising web site factors and several customer and intermediary factors, to explain the main and moderating effects of the online medium on customer price sensitivity. We use this framework to develop hypotheses, which we test using data from both online and offline customers in the hospitality industry. We examine two important aspects of price sensitivity: the relative weight a customer attaches to price compared to other attributes (price importance), and customer’s perceived value of

acknowledge the helpful input of Alan Cooke. 3 Bundle offers consisting of two or more products often include a price discount. The impact of the price discount on the perceived attractiveness of the bundle has been shown to depend on which product is discounted. It has been argued that discounts are more effective when they are assigned to the product that will receive the most weight in the overall evaluation of the bundle. We propose that the perceived value of the discount may also depend on a referent specific to each product. Six studies are used to provide evidence that (1) price discount framing effects can be explained by reference dependence and (2) that reference dependence and product importance independently contribute to price discount framing effects. 4 Guiltinan (1987, p. 74) broadly defines bundling as “the practice of marketing two or more products and/or services in a single package for a special (i.e., lower) price. ” There are numerous examples of situations where this practice is employed. For example, banks offer lower priced insurance, credit cards, and financial services when these products are purchased as a bundle. Resorts offer lower priced airfare, accommodations, and event tickets when these products are purchased as a bundle. Software companies offer lower prices on suites of software

www.elsevier.com/locate/obhdp Why more can be less: An inference-based explanation for hyper-subadditivity in bundle valuation q

Needs-driven service bundling in a multi-supplier setting The computational e 3 service approach Sybren de KinderenVRIJE UNIVERSITEIT Needs-driven service bundling in a multi-supplier setting The computational e 3 service approach ACADEMISCH PROEFSCHRIFT ter verkrijging van de graad Doctor aan de Vrije Universiteit Amsterdam, op gezag van de rector magnificus prof.dr. L.M. Bouter, in het openbaar te verdedigen ten overstaan van de promotiecommissie van de faculteit der Exacte Wetenschappen op maandag 25 oktober 2010 om 13.45 uur in de aula van de universiteit,

To satisfy complex consumer needs, services increasingly are composed out of more elementary services offered by different suppliers, thereby allowing each supplier to focus on his core competences. When business developers engage in developing such multi-enterprise service bundles, the number of possible ways to do business soon increases, and hence a need arises for automated support for reasoning about commercially viable service bundles and related business models, implying also a selection of partners that provide these services. We present a conceptual, model-based, framework for automating such reasoning, and a fourstep method for using this conceptual framework to perform a financial feasibility analysis of business models for cross-organizational service bundles. We discuss and exemplify theoretical fundaments for such a method, using a real-life case study in the energy sector.