Does Product Platforming Pay Off?
Bibliographic record
Abstract
Product platforming—a specific approach to new product development utilizing common technology components or subsystems deployed across multiple products or product lines—has been argued to bring numerous valuable organizational outcomes (e.g., effectiveness of R&D process, superior postlaunch product commercial performance, and ultimately sustained competitive advantage). Yet, large‐scale longitudinal empirical examinations of the mechanisms linking product platforming to firm performance are scarce. Drawing on the concepts of architectural leverage and product life cycle flexibility, the article presents the development and empirical test of a set of hypotheses regarding the commercial outcomes of platforming at the product level using a unique dataset comprising all products developed and sold by a large, global LED lighting manufacturer in 2010–2015. The results suggest that platformed products demonstrate significantly higher sales and gross profit margins aggregated over their product life cycle (PLC), vis‐à‐vis the comparable group of nonplatformed, individually developed products. In addition, the findings demonstrate that a product platforming development approach appears to extend the PLC relative to nonplatformed products based on an integral, nonmodular product architecture.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".