An evolutionary perspective on capabilities for fluid product-markets: The contingent effects of routinization and renewal in marketing, R&D, and operations
Bibliographic record
Abstract
The performance benefits of functional capabilities in marketing, technology, and operations rely on their routinization in organizational processes, but these also require renewal in response to environmental change. This raises a fundamental tension: is it better to maximally develop functional capabilities that offer the highest contingent benefit in present market conditions, and/or to modify capabilities as conditions change? We propose two measures of a firm's ability to renew its functional capabilities to align with market conditions: capability heterogeneity (variation in extant capabilities) and capability adaptability (selection among these strategic options). In a 20-year panel of 771 firms, we find environmental change increases the importance of these aspects of how capabilities are managed relative to what capabilities a firm possesses: In stable product-markets, capability heterogeneity and adaptability incur significant costs whereas functional capabilities improve profitability. In contrast, functional capabilities can be detrimental in fluid product-markets whereas heterogeneity and adaptability increase profitability. Notably, marketing capability remains beneficial across environments, acting as a profitable alternative to capability heterogeneity and adaptability when future conditions are uncertain. This evolutionary perspective contributes to ongoing theoretical debates on the conceptualization and consequences of capabilities, with practical implications for mitigating the risks of excessive inertia or change.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| 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".