Dynamic Managerial Capabilities, Resource Orchestration, and Performance: A Research Proposal
Bibliographic record
Abstract
In this research study, we propose a theoretical model and a set of testable hypotheses that examine the relationships among dynamic managerial capabilities, orchestration of technological (digital and non-digital) resources, and performance. In the model, we posit that dynamic managerial capabilities of human capital, cognition, and social capital interact to facilitate the orchestration of organizational technological (digital and non-digital) resources, which involves search and selection, structuring, bundling, and leveraging of resources, and leads to higher performance. In addition, we suggest that intuition plays its part along with the three underpinnings of dynamic managerial capabilities in resource orchestration and performance. Further, we contend that the orchestration of technological (digital and non-digital) resources is impacted by key environmental factors such as environmental munificence, technological turbulence, and competitive intensity. Furthermore, we argue that technological (digital and non-digital) capabilities may mediate the relationship between resource orchestration and performance. Since the model integrates effects at the individual and organizational levels, we propose to employ multi-level modeling as it improves the accuracy of findings and understanding of the phenomena across different levels.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".