In with the old, in with the new: capabilities, strategies, and performance among the Hollywood studios
Bibliographic record
Abstract
Abstract An increased focus on turbulent environments has led to a growing interest among researchers in the concept of dynamic capabilities. In this study, we approach dynamic capabilities in a framework of two complementary processes. On one hand, firms can build upon existing capabilities in products and markets in which they have experienced recent success; on the other hand, they can also intentionally focus on other products and markets in which they seek to build capabilities to address their lack of recent success. We examine these two processes within project‐based industries and identify replication and renewal as two types of strategies that firms use to add a dynamic component to their capabilities. We also theorize that the success of each of these strategies is tied to differentiation from rivals, and to firm‐level resource availability and industry‐level demand characteristics. We test these propositions by focusing on the film genres that were offered by the Hollywood studios over a thirty‐year period. Copyright © 2009 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".