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Incorporating Expectations as a Basis for Business Service Selection

  • Adel M. ElMessiry
  • Xibin Gao
  • Munindar P. Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6470)

Abstract

The collaborative creation of value is the central tenet of services science. In particular, then, the quality of a service encounter would depend on the mutual expectations of the participants. Specifically, the quality of experience that a consumer derives from a service encounter would depend on how the consumer’s expectations are refined and how well they are met by the provider during the encounter. We postulate that incorporating expectations ought therefore be a crucial element of business service selection.

Unfortunately, today’s technical approaches to service selection disregard the above. They emphasize reputation measured via numeric ratings that consumers provide about service providers. Such ratings are easy to process computationally, but beg the question as to what the raters’ frames of reference, i.e., expectations. When the frames of reference are not modeled, the resulting reputation scores are often not sufficiently predictive of a consumer’s satisfaction.

We investigate the notion of expectations from a computational perspective. We claim that (1) expectations, despite being subjective, are a well-formed, reliably computable notion and (2) we can compute expectations and use them as a basis for improving the effectiveness of service selection. Our approach is as follows. First, we mine textual assessments of service encounters given by consumers to build a model of each consumer’s expectations along with a model of each provider’s ability to satisfy such expectations. Second, we apply expectations to predict a consumer’s satisfaction for engaging a particular provider. We validate our claims based on real data obtained from eBay.

Keywords

Support Vector Machine Service Selection Reputation System Service Encounter Text Fragment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Adel M. ElMessiry
    • 1
  • Xibin Gao
    • 1
  • Munindar P. Singh
    • 1
  1. 1.North Carolina State UniversityRaleighUSA

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