Examining the Influence of Operational Intellectual Capital on Capabilities and Performance
Bibliographic record
Abstract
Managers have long been challenged by an abundance of internal and external demands and uncertainties in their operating environments. Anecdotal evidence and a growing number of research studies have advocated process flexibility and product innovation as organization-level operating capabilities critical for responding to such demands and uncertainties, and have highlighted the need for more efficient and effective management of the firm's knowledge-based resources. Leveraging arguments from the resource-based and knowledge-based views of the firm, we introduce a second-order latent construct called operational intellectual capital, which represents the organization's operating know-how embedded in a system of complementary (i.e., covarying) knowledge-based resources. We argue that operational intellectual capital influences organization-level operating capabilities such as process flexibility and product innovation, which, in turn, influence business performance. We empirically examine these relationships using structural equation modeling on a cross-section of U.S. manufacturing survey data. Statistical results from the estimation of a coalignment model and comparisons with several other models support our operational intellectual capacity conceptualization and its impact on operating capabilities and business performance, respectively. Our research thus suggests the importance of possessing and leveraging a system of complementary knowledge-based operating resources, and addresses the need for the reformulation of operations strategy theory in terms of the emergent knowledge-based view of the firm.
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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 | 0.004 | 0.031 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".