Trade-off Service Portfolio Planning – A Case Study on Mining the Android App Market
- Published
- Accepted
- Subject Areas
- Software Engineering
- Keywords
- Service portfolio planning, Open Innovation, Crowdsourcing, Kano analysis, Optimization, OTT services.
- Copyright
- © 2015 Nayebi et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. Trade-off Service Portfolio Planning – A Case Study on Mining the Android App Market. PeerJ PrePrints 3:e1354v1 https://doi.org/10.7287/peerj.preprints.1354v1
Abstract
Service portfolio planning is the process of designing collections of services and deciding on their provision. The problem is highly information intensive, and most of the information required is hard to gather. In this paper, we present a solution approach based on the paradigm of Analytical Open Innovation (AOI). Open innovation is a cheap and low risk problem solving approach which relies on knowledge exchange with outside of company as a competitive advantage. Different forms of open innovation; crowd source, open source and outsource; facilitate the provider and consumer interactions and brings higher customer value. In our proposed AOI approach, open innovation is utilized for elicitation of services from web data, crowdsourcing the service value from potential customers and for the estimation of service implementation effort. For service evaluation, we apply the Kano theory of product development and customer satisfaction. Based on that and as the result from an optimization process, resource-optimized service portfolios are created that constitute trade-offs in balancing between gained value and effort needed. As a proof of concept, the proposed approach is illustrated via a case study project for the composition of Over the Top TV (OTT) services. The atomic services from 241 qualified apps were analyzed from the android app market. We demonstrate that the proposed approach is able to generate optimized trade-off solutions, composing better apps at each capacity level and achieving better customer satisfaction .The level of improvement in customer satisfaction varies between 16.5% and 95.3%.
Author Comment
This is the second version of our study. The study is going to be submitted to JSS journal very soon and we are looking for feedbacks into this study.