Sales, Marketing, and Research-and-Development Cooperation across New Product Development Stages: Implications for Success
Bibliographic record
Abstract
Prior research has identified the integration of marketing with research and development (R&D) as a key success factor for new product development (NPD). However, prior work has not distinguished the sales and marketing functions, even though they are distinctive departments within an organization. Therefore, the authors extend prior research and examine the effect of cross-functional cooperation among sales, marketing, and R&D on NPD performance across multiple stages of the NPD process. The authors use multiple-informant data from 424 sales, marketing, and R&D managers as well as project leaders of 106 NPD projects to test several hypotheses. The results show that the cooperation between sales and R&D and between sales and marketing has a significant, positive effect on overall NPD project performance beyond marketing–R&D cooperation. The authors also find that the effect of cross-functional cooperation among sales, marketing, and R&D on overall NPD project performance varies across stages of the NPD process. More specifically, the authors find that sales–R&D cooperation in the concept and product development stages is critical for greater new product success. Sales–marketing cooperation is important in the concept development stage but has surprisingly less impact in the implementation stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".