Thank you for the bad news: Reducing cynicism in highly identified employees during adverse organizational change
Bibliographic record
Abstract
Adverse changes, such as layoffs or wage cuts, can irremediably damage the relationship between employees and their organization. This makes it all the more important for organizations to provide information about these changes to avoid the emergence of organizational cynicism among their employees. Drawing on uncertainty management theory, we argue that informational justice and organizational identification jointly regulate organizational cynicism in the context of adverse change. In addition, we examine whether informational justice influences employee exit intentions through cynicism. We test our hypotheses using a multi‐method approach, encompassing one experiment (Study 1), one large‐scale survey of 1,795 employees undergoing a major restructuring (Study 2), and a five‐wave field survey of 174 workers undergoing layoffs and wage cuts (Study 3). In all three studies, poorer communication from the organization predicted greater exit intentions through increased cynicism for employees who were more (rather than less) identified with the organization. By integrating the literature on informational justice, organizational identification, and cynicism, our research offers a more nuanced understanding of the antecedents and consequences of cynicism in the context of adverse organizational change. Practitioner points Organizations undergoing adverse changes, such as layoffs and wage cuts, should provide employees with timely and detailed explanations for the changes (i.e., informational justice). When employees do not receive timely and detailed explanations for adverse changes, they are more likely to become cynical, and to decide to leave the organization. Providing adequate explanations is especially important for employees who strongly identify with the organizations because they are more sensitive to informational justice. Providing explanations is not as effective in reducing cynicism among employees with low levels of organizational identification. When organizations fail to explain adverse changes, employees who identify strongly with the organization may become as cynical as employees whose identities are less closely tied to the organization.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".