Video methods in strategy research: Focusing on embodied cognition
Bibliographic record
Abstract
Research summary : Video‐based methods can help researchers explore the significance of the human body in the practice of organizational strategy. We present a toolkit for the analysis of video data. The toolkit consists of three techniques: the detailing of significant forms in visual data, the sequencing of movement around such forms, and the patterning of movements across episodes. We employ these techniques on video recordings of middle managers engaged in a large‐scale strategic change effort. By revealing and analyzing the practices of “top‐down” and “bottom‐up bridging,” we show how the embodied cognition of middle managers supports strategy implementation by influencing nascent behavioral and cognitive changes among their subordinates. We conclude our account by suggesting a research agenda for video‐based work in strategy research . Managerial summary : We explore video-based methods as a means to understand how middle managers incorporate strategic change in their practice. During strategic change efforts, a lot of what is taken to be the buildup of “shared understanding” is more appropriately viewed as emotional contagion. The success of middle management change agency is thus measured, not only by their successful elaboration of strategy content to others, but also whether or not organizational members invest themselves emotionally to the change endeavor. Such contagion is founded on the interaction of human bodies as well as their verbal discourse . Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".