Organizational Environments in Flux: The Impact of Regulatory Punctuations on Organizational Domains, CEO Succession, and Performance
Bibliographic record
Abstract
A central debate in organizational theory concerns how organizations evolve. There are two diametrically opposing viewpoints. Adaptation theories predict that change occurs as fluid organizations adjust to meet shifting environmental demands, while selection theories predict that change occurs through the differential selection and replacement of inert organizations as environmental demands vary over time. Our paper bridges these polar opposites by using a punctuated equilibrium framework to examine organizations' responses to discontinuous industry-level change. This framework recognizes that the histories of many industries are occasionally punctuated by dramatic exogenous shocks, such as radical technological innovation, social and political turmoil, major changes in government regulation, and economic crashes. Our central thesis is that such environmental punctuations dramatically reduce pressures and rewards for organizational inertia and thereby alter both organizations' propensities for change and their survival chances following change. We focus on one form of punctuation, major regulatory change, and study firms in two industries: general hospitals and savings and loan associations. For organizations in both industries, we examine three important outcomes: shifts in organizational domain, CEO succession, and changes in financial performance. Our analyses show that punctuational regulatory change prompts shifts in organizational domains and executive leadership. Additionally, post-punctuation domain change and post-punctuation CEO succession both affect subsequent performance. We discuss our results in light of current thinking about the content and process effects of core organizational change, which has been developed in the context of stable environments. Finally, we argue for the development of more temporally sensitive theories of organizational action.
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.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".