The architecture of marketing leadership: how different structures of marketing presence in the top management team drive new product performance
Bibliographic record
Abstract
Purpose Prior research seldom explores the different structures of marketing presence in the top management team (MPTMT) and their impact on new product performance. In this paper, we distinguish among three structures of MPTMT: (1) a dedicated MPTMT; (2) a joint marketing and sales MPTMT; and (3) a joint marketing and other operations MPTMT. We then examine how these three structures of MPTMT are related to cross-functional integration in NPD and, subsequently, new product performance. Design/methodology/approach Path analysis is used to test the model using data collected from 139 U.S. manufacturing firms. We conducted two rounds of survey data collection (with a one-year gap) to assess the potential effect of common method variance. Findings The results show that, compared with no MPTMT, all MPTMT structures positively affect cross-functional integration in NPD, which, in turn, enhances new product performance. However, joint MPTMT structures have a greater impact than a dedicated MPTMT. Our moderation analysis also reveals that as TMT heterogeneity increases, the effect of dedicated MPTMT diminishes, but the effects of the other two joint structures remain positive and stable. Research limitations/implications The model could include alternative mediating organizational processes and performance outcomes. Practical implications The findings provide managers with insight on how to configure and leverage marketing influence in the upper echelons in both SMEs and large firms. Originality/value The findings of this study highlight the importance of delineating MPTMT structures, understanding how they create value, and specifying their boundary conditions.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".