Organizational Structure, Subsystem Interaction Pattern, and Misalignments in Complex NPD Projects
Bibliographic record
Abstract
Developing a complex new product requires the firm both to deconstruct that product into subsystems and to create an organizational structure aligned with the product architecture. However, empirical evidence indicates that misalignments do occur and are usually one of two general forms: a “hidden dependency,” which is a missing link between teams responsible for two interacting subsystems; or “spurious communications” between two teams that interact even though their respective subsystems are not linked. We model the product development process as a search on a rugged landscape and study how misalignments affect the performance of the process in both design quality and convergence time. We find that the effects are mediated by the organizational decision‐making structure, and also by the interaction pattern among product subsystems. For instance, with a modular design, a project with a hidden dependency yields higher quality design solutions than a project with spurious communications or an aligned project. However, hidden dependencies cause a longer convergence time. Further, in modular designs spurious communications do not impact quality or convergence time when compared with aligned projects. The effect in non‐modular product designs depends on the organizational decision‐making structure and managerial capability. When decisions are made in a centralized organization that employs a capable manager, spurious communications improve the design quality but could delay the convergence time. We trace the cause of these effects to errors committed by teams in rejecting superior designs, which make the search process more exploratory and covering a wider area of the search landscape.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".