Dynamic Managerial Capabilities, Resource Orchestration, and Performance: A Research Proposal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it