An open innovation approach in support of product release decisions
Bibliographic record
Abstract
Release decisions are of pivotal importance for product success in incremental and iterative software development. In this paper, the wickedness of these decisions is approached by a collective problem solving process. The paradigm of Open Innovations is emphasizing the range of opportunities available to get access to distributed knowledge and information. In particular, we apply (i) Analytical Open Innovation for information gathering and (ii) Morphological Analysis (MA) for problem structuring. The proposed decision support methodology is illustrated by a comprehensive case study. In the context of OTT service delivery, planning of both features and their different functionality levels is studied. From the broad involvement of stakeholders in the whole formulation, structuring and solution process, a higher validity and customer value of the developed products is demonstrated. Without performing MA, the proposed feature implementations would include inconsistencies and thus create customer and user concerns. Furthermore, from community based detection of cost and value synergies, potential resource savings and additional value creation opportunities are utilized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".