Architecture, attention, and adaptation in the multibusiness firm: General electric from 1951 to 2001
Bibliographic record
Abstract
Abstract In this study, we analyze how the organizational architecture of a multibusiness firm affects the adaptation of its constituent business units. Using an inductive analysis of GE's governance system from 1951 to 2001, we examine how the integration of corporate and business unit attention occurs within and across the firm's governance channels. Our theory identifies an unexplored aspect of the M‐form's architecture: collective vertical interactions between the corporate office and business units through cross‐level channels. Overall, we articulate three types of channel integration—cross‐level, cross‐functional, and channel coupling—and examine their effects on responsiveness to threats and opportunities. We find that despite an elaborate organizational architecture, there were periods when GE's governance system did not allow for coordination of corporate and business unit agendas. Our theory proposes that the temporal coupling of specialized, cross‐level channels creates an organizational architecture that is both differentiated and integrated. This architecture integrates levels and issues simultaneously, yet focuses attention sequentially, providing more effective conditions for joint attention and coordination between the corporation office and the business unit and adaptive change at the business unit level. Copyright © 2012 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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.002 |
| 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".