ARCHITECTING VALUE CREATION: A STRATEGIC GOVERNANCE FRAMEWORK FOR PRODUCT MANAGEMENT IN SCALABLE TECHNOLOGY ENTERPRISES
Bibliographic record
Abstract
Abstract As technology enterprises scale, the complexity of coordinating innovation, capital allocation, and market responsiveness increases exponentially. While product management has traditionally been positioned as a feature-oriented coordination role, scalable organizations increasingly rely on product leaders to architect value creation across engineering, finance, and go-to-market systems. This paper advances a strategic governance framework that reconceptualizes product management as an enterprise-level decision architecture function rather than an operational intermediary. Drawing from strategic management theory, corporate governance principles, and platform economics, the study develops an integrated model for aligning product portfolios with long-term enterprise value. The framework articulates how product management can function as a mechanism for strategic intent translation, portfolio coherence, capital efficiency, and adaptive risk management. By positioning metrics, roadmaps, and cross-functional authority structures as governance instruments, this paper contributes a novel perspective to both academic discourse and executive practice. The findings suggest that scalable technology enterprises achieve sustained value creation not through isolated innovation, but through disciplined product governance systems that integrate strategic foresight with measurable economic outcomes.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".