Software product line market repositioning: The power of functional groups
Bibliographic record
Abstract
This is a longitudinal study of change process as it applies to software product line evolution. The objective is to study and describe changes in software product line that occurred after top management team of a supplier of hardware and software for telecommunication equipments decided to change the target market for its software intensive telecommunication products as a result of market decline. This company has a proven record of innovation and technological breakthroughs and has offices in Europe, North America, Africa, and Asia. The study is divided into three phases. The next phase in this study is to look at the relationships between functional groups and to try and answer the question: “Does the power of functional groups closest to the customer increases during sales declines?” The analysis of the data available to us shows that functional groups that were closer to the customers increase their relative size; groups located in remote sites decrease in size faster than the groups with similar skills located at the company's headquarters; and groups that were more involved in developing products and have specialized skills decreased in relative size. Our final analysis shows that the power of functional groups that interact most frequently with customers increases while the power of functional groups that interacts the least with customers decreases.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".