Trade-off Service Portfolio Planning – A Case Study on Mining the Android App Market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it