An Evaluation of Research on Integrated Product Development
Bibliographic record
Abstract
Integrated Product Development (IPD) creates overlap and interaction between activities in the new product development process and, because this increases the need to coordinate, compensates through other aspects of the new product development process (e.g., integrated tools), product definitions (e.g., incremental development), organizational context (e.g., reduced task specialization), and teaming (e.g., cross-functional teams). Since IPD has become an important new standard for managing new product development, this paper's general aim is to evaluate the research that has been conducted on it. Our three specific objectives include first critiquing the IPD literature by identifying problems with empirical research and recommending solutions. There are concerns about the overall approach, conceptualizing and operationalizing IPD characteristics, and selecting performance objectives. Second, we conduct a meta-analysis to evaluate relationships between specific IPD characteristics and project performance. We indicate where relationships do or do not exist and identify variables that may moderate these relationships. Third, we offer suggestions for extending IPD research into studies of (a) the hierarchy of teams working on a project, (b) one company managing a portfolio of projects over time, and (c) two or more firms collaborating in a strategic alliance.
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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 | 0.180 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".