Seeing the good in the bad: A self-affirmation model of organizational dehumanization.
Bibliographic record
Abstract
Organizational dehumanization has traditionally been conceptualized as a negative phenomenon that leads to undesirable consequences. In this research, we depart from this perspective and test the possibility that organizational dehumanization may also have an unexpected silver lining effect that extends beyond the workplace to benefit other individuals in employees' social sphere. Drawing upon self-affirmation theory, we propose that organizational dehumanization threatens employees' self-worth. To restore their self-worth, employees will reflect on their core values and social relationships, motivating them to engage in prosocial behaviors toward society (i.e., increased volunteering) and their family (i.e., increased family task performance), respectively. We anticipate that these effects will be more pronounced for employees whose self-worth is contingent on their ability to effectively help others. We test our hypotheses in two phases, encompassing six studies that employ complementary methodologies. In the first phase, we adopt a manipulation-of-mediator design that involves five interrelated experiments. In Study 1, we manipulate organizational dehumanization and measure self-worth threat; in Studies 2 and 3, we manipulate self-worth threat and measure self-affirmation of core values and social relationships, respectively; in Study 4, we manipulate self-affirmation of core values and measure volunteering; in Study 5, we manipulate self-affirmation of social relationships and measure family task performance. In the second phase, we conduct a field study (Study 6) in which we collect four-wave, dyadic (i.e., employee-partner/spouse) data aimed at testing the full moderated serial mediation model. All studies support our hypothesized relationships. Implications of our findings for theory and practice are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".