High-Performance Product Management: The Impact of Structure, Process, Competencies, and Role Definition*
Bibliographic record
Abstract
Product management is one of the most important functions in marketing. Yet the product management literature has focused largely on creating successful products and has relatively little to say about creating effective product management organizations. This paper focuses on the organizational determinants of high-performance product management at three levels: (1) the product manager as an individual; (2) the marketing processes related to product management; and (3) the organization structure and role definition. The paper identifies several key factors that potentially impact product management performance. A set of qualitative interviews is conducted to develop hypotheses related to constructs that may drive product management performance. These hypotheses are used to develop a causal model for product management performance that includes constructs related to roles and responsibilities, organization structure, and marketing processes related to product management. An empirical survey of 198 product managers from a variety of industries is conducted to test the causal model. The results of the causal model suggest that performance of a product management organization is driven by structural barriers in the organization, the quality of marketing processes, roles and responsibilities, and knowledge and competencies. The findings suggest that structural boundaries and interfaces are the biggest impediment to effective product management, followed by clarity of roles and responsibilities. The research highlights the importance of organization structure and effective human resource practices in improving product management performance.
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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 | 0.017 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".