Have the Factors Affecting Software New Product Development (S-NPD) Changed in the Age of Mobile Apps and Agile Methods?
Bibliographic record
Abstract
It has been long recognized that Time-to-Market, Cost-of-Delay, and Uncertainty are the three major factors that can impact New Product Development (NPD) process. Software New Product Development (S-NPD) process is even more notorious with delay and cost overrun. The goal of this position paper is to examine the three factors: Time-to-Market, Cost-of-Delay and Uncertainty through “observation” vis-à-vis software Apps development process and the use of Agile methods in order to see if something has changed over the years. In addition, a controlled group interview is conducted to support the observation method. The questions this paper seeks to answer are: Are we getting to market on time? Are we delivering software product on budget? Do we have reduced uncertainty when it comes to Apps development and the use of Agile method? The results obtained show that the use of Agile approach in developing Apps can take the product to the market quicker compared to using the rigid linear process, in our case the waterfall V-model. Also, the “product introduction delay” as part of product performance was measured on the assumption that there is no budget or cost overrun. In this case, the Agile method performs better than the linear approach. Lastly, in terms of technological uncertainty, the Agile development approach does not necessarily bring the expected commercial success, and in fact, for the situation where the development of Apps is involved, rapid development could result in failure if proper plan is not in place for the evolution of the product.
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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 | 0.017 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".