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Analytical Open Innovation for Value-Optimized Service Portfolio Planning

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Software Business. Towards Continuous Value Delivery (ICSOB 2014)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 182))

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Abstract

Service portfolio planning is the process of designing collections of services and deciding on their provision. The problem is highly information and decision centric. In this paper, we present a solution approach called Analytical Open Innovation (AOI). Open innovation facilitates comprehensive crowdsourcing and social media information analysis. In our proposed approach (AOI), open innovation is utilized for the elicitation of service needs, the definition of quality provision levels for each of the services, and detection of service dependencies and cost and value synergies. As the result of a rigorous optimization process, diversified and resource-optimized service portfolios are created. As a proof of concept, the proposed approach is illustrated via a case study project using Over the Top TV (OTT) services to be offered at different levels of quality over four quarters of a year.

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Author information

Authors and Affiliations

  1. Software Engineering Decision Support Laboratory, University of Calgary, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4, Canada

    Maleknaz Nayebi & Guenther Ruhe

Authors
  1. Maleknaz Nayebi
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  2. Guenther Ruhe
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Editor information

Editors and Affiliations

  1. Aalto University, Aalto, Finland

    Casper Lassenius

  2. Lappeenranta University of Technology, Lappeenranta, Finland

    Kari Smolander

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Nayebi, M., Ruhe, G. (2014). Analytical Open Innovation for Value-Optimized Service Portfolio Planning. In: Lassenius, C., Smolander, K. (eds) Software Business. Towards Continuous Value Delivery. ICSOB 2014. Lecture Notes in Business Information Processing, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-319-08738-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-08738-2_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08737-5

  • Online ISBN: 978-3-319-08738-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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