Becoming A Leader In A Complex Organization
Bibliographic record
Abstract
For a new leader, the process of entering and establishing a position of leadership in a complex organization presents a major challenge. This challenge seems particularly acute when authority, goals and technology are ambiguous, as in many professional service organizations. In this paper, we integrate ideas from the literature on socialization and role theory as well as that on executive succession processes to view new leader integration as a mutual adjustment process between two trajectories – that of the organization and that of the new leader. It is argued that this may lead to four possible types of integration outcomes: assimilation, transformation, accommodation and parallelism. Drawing on a case study of a large hospital, the paper identifies several mechanisms that can be mobilized by the new leader to enhance his or her room for manœuvre as the integration process evolves. The mechanisms can be classified as collaborative or affirmative, with each type having different risks and advantages. The case analysis further reveals that leader integration processes may be differentiated between different activity domains, dynamic over time (as the use of one type of integration approach alters the potential for another later), and interactive across different activity domains (as events in one part of the organization have consequences for those occurring in another).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".