Strategizing through the capability lens: sources and outcomes of integration
Bibliographic record
Abstract
Purpose This paper aims to explore the concept of capabilities and where they come from as well as their impact on integration and performance. Design/methodology/approach The paper is presented in the form of a theoretical development and literature review. Findings This paper proposes a theory of capability development and discusses the conditions under which a capability is effective. In particular, for a capability to be effective both local and global coherence are required. But a capability effectiveness and coherence has an inverted U shape. It increases with coherence up to a certain threshold then decreases. As a result, the development of capability is a powerful integration mechanism that crosses levels and functions. Research limitations/implications This is a theoretical paper; the propositions offered have still to be empirically tested. Practical implications Opening up the capability black box might help managers better grasp how to develop and shape organizational capabilities that are deemed to contribute to competitive advantage (e.g. the pricing capability). First, capabilities are not to be equated with competitive advantage. They may lead to a competitive advantage only where the context is favorable. Thus consistency with the environment challenges is an important factor to watch. This suggests that managers should give attention to the relationships between what they perceive to be their capabilities and the nature of the challenges faced by the organization. Further this research might promote the development of tools to measure coherence within a context and manage appropriate levels of dissent to trigger the re‐shaping of existing capabilities or the emergence of new one. Originality/value The paper bridges highly theoretical questions with practical considerations.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".