Organizational change failure: Framing the process of failing
Bibliographic record
Abstract
Despite what we know about how organizations and their members respond to change, organizations continue to spend an inordinate amount of time confronting, mitigating, and dealing with failure during change. This special issue focuses on what happens when organizational change fails. Its goal is to enhance knowledge and advance theory regarding the processes and mechanisms that underlie the emergence of organizational change failure. In this editorial, we first take stock of the established perspectives on failure, and introduce an integrative approach to offer a more holistic account of the process of change failure. The framework constitutes a multilevel, interlocking strategy for future scholarship. It highlights how the evolving experience defines, creates, and enacts failure during change across three structures: the surface (i.e., context), intermediate (i.e., building block dimensions), and deep (i.e., enduring aspects) structures of failure. With this frame as its basis, the articles in the special issue prompt discussion of what exactly failure means for organizations and their members dealing with different accounts of change failure.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".