Service Value Networks: Unleashing the Combinatorial Power of Service Mashups

Kraemer, Jan; Conte, Tobias; Dinther, Clemens van; Weinhardt, Christof

doi:10.2139/ssrn.1582902

Cited by 5 publications

(7 citation statements)

References 41 publications

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“…In SVNs, the coordination and orchestration of services is performed automatically by a universally accessible network orchestration platform. Thus, Krämer et al (2010) define SVN as follows: ''Service Value Networks are Smart Business Networks that provide business value by performing automated on-demand composition of complex services from a steady, but open pool of complementary as well as substitutive standardized service modules through a universally accessible network orchestration platform.'' Two parts of this definition are especially important for present purposes: first, service modules need to be standardized and accessible through a network; second, the composition needs to be automated.…”

Section: Related Researchmentioning

confidence: 99%

“…SVNs are components of an emerging e-Service form and have been studied recently by Blau, Krämer, et al (2009) and Krämer et al (2010). SVNs focus on ICT (or electronic) services, since these can be easily combined through plug-andplay functionality, which is harder for non-electronic services (based on the lack of standardization and/or communication between single service components).…”

Section: Introductionmentioning

confidence: 99%

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Strategic Learning by e-Service Suppliers in Service Value Networks

Haas

Kimbrough

Dinther

2012

Journal of Service Research

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This article examines strategic behavior of e-Service suppliers that offer electronic services in complex Service Value Networks (SVNs). In SVNs, consumers request a bundle of e-Services, and the SVN acts as an aggregator of single service instances, automatically configuring services from different e-Service suppliers into a complex service bundle, which is then offered to the consumers. In this context, e-Service suppliers who want to maximize their business success need to configure their services according to the (for them unknown) preferences of the consumers. Current literature, however, does not offer much guidance on how to find a fitting service offer in this situation, especially when the suppliers are faced with a changing consumer base. For this reason, we study two specific learning regimes that are able to capture and deal with the inherent complexity of the corresponding strategy space. Besides finding beneficial service offers, e-Service suppliers might also learn collusive behavior if it aligns with individual incentives. The potential occurrence of such behavior is the second aim of our work. Our results show that even with relatively simple learning regimes, e-Service suppliers are able to find beneficial offers and learn to collude tacitly (and presumably legally), which increases their profits.

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Section: Related Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Strategic Learning by e-Service Suppliers in Service Value Networks

Haas

Kimbrough

Dinther

2012

Journal of Service Research

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“…SVNs are networked economies constituted of distributed service providers and consumers. In these environments, services are dynamically composed into value-added complex services fulfilling the need of a long-tail of individual customers [5].…”

Section: Introductionmentioning

confidence: 99%

Efficient QoS Aggregation in Service Value Networks

Haak

2012

2012 45th Hawaii International Conference on System Sciences

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In recent years, the trend towards standardization, simplification and modularization in the service sector has fostered the raise of Service Value Networks where providers and consumers jointly co-create value. With many different competing services available, the user experience, which is captured by the non-functional Quality-of-Service (QoS) attributes, is an important competitive factor. QoS computation for complex Web services, i.e. the aggregation of QoS factors from atomic services, is essential for an automated an optimized Web service selection process. However, the computational complexity of QoS aggregation has often been disregarded in the respective field of research, whereas computational efficiency is inevitable for the application of optimization approaches in on-line scenarios. The threefold contribution of this paper consists of an elaboration on the computational complexity of aggregating QoS, an approximation scheme that allows for a computational efficient optimization and a broad analytical and simulation-based evaluation of this approach.

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“…SVNs, as a special form of of Business Networks, are increasingly highlighted in both research and industry contexts [6]. With respect to the latter, service platforms such as salesforce.com's AppExchange 1 or the Chinese platformas-a-service market Alisoft.com 2 rapidly push into the (software) service market, continuously increasing their market shares.…”

Section: Introductionmentioning

confidence: 99%

“…(n−k)! alternative complex services may be created, each of which potentially meets a specialized demand [6]. That way, service providers are able to exploit economies of scope [11] as their offerings are not designed to be included in one or few complex services, but contribute to various solutions capturing diversified customer needs.…”

Section: Introductionmentioning

confidence: 99%

Co-Opetitive Contracting in Service Value Networks

Conte

2011

2011 IEEE 13th Conference on Commerce and Enterprise Computing

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Based on the graph-based Vickrey auction in service value networks introduced by Blau et al. [1], we analyze strategic behavior of service providers in networked formations regarding their opportunities to foster bilateral cooperative agreements. Introducing enforceable ex-ante agreements between adjacent service providers in a Service Value Network (SVNs), we investigate beneficial investment strategies that aim at reducing communication cost between sequentially composed services. The contribution at hand is twofold: Based on the foundation of SVNs and the efficient allocation of services through a graph-based Vickrey auction as presented in Blau et al. [1], [2] we (i) develop a model of cooperation and show analytically that, in the overall non-cooperative setting at hand, our model of cooperation implements a mutual weakly dominant strategy for adjacent service providers to reduce the cost of communication by customizing their service offerings. Transferring our results into practice, we (ii) demonstrate the valuable properties of our model of cooperation by providing a comprehensive application scenario.

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Service Value Networks: Unleashing the Combinatorial Power of Service Mashups

Cited by 5 publications

References 41 publications

Strategic Learning by e-Service Suppliers in Service Value Networks

Strategic Learning by e-Service Suppliers in Service Value Networks

Efficient QoS Aggregation in Service Value Networks

Co-Opetitive Contracting in Service Value Networks

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